Ferdowsi University of MashhadJournal of Geography and Environmental Hazards2322-168214320250923Identification of Dust Sources Using Spatio-Temporal Tensor of Aerosol Optical Depth in Ilam CountyIdentification of Dust Sources Using Spatio-Temporal Tensor of Aerosol Optical Depth in Ilam County1174441910.22067/geoeh.2023.82988.1383FARaminKaramiM.Sc. Student, Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, IranHosseinEtemadfardAssistant Professor, Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran0000-0003-3805-1711Journal Article20230617One of the main factors contributing to air pollution is the phenomenon of dust, which causes significant damage to various economic, social, and human resources. Dust events occur in different parts of the world, especially in arid and semi-arid regions, and are driven by both natural and human factors. This research aimed to identify the origin of dust in Ilam city using the spatio-temporal tensor of aerosol optical depth (AOD) derived from MODIS sensor data during the period March–June 2022. First, dusty days were extracted from meteorological data, and the spatio-temporal tensor of aerosol optical depth was generated. The reason for using the tensor was to examine large volumes of data across both spatial and temporal dimensions simultaneously. The results of comparing the tensor with corresponding meteorological data showed that whenever the aerosol optical depth exceeded 0.5, dust events occurred. The spatio-temporal dust tensor analysis revealed that dust levels are directly related to wind speed, and that dust events occur when wind speed exceeds 15 m/s. Finally, by identifying the spatial variations in AOD, four dust sources were detected in the study area (Beld, Mesopotamia, Misan, and Wasit), with Mesopotamia recognized as a major potential source. The analysis of the temporal pattern of AOD indicated an increasing trend in May. The highest AOD value, 3.85 in May, reflected the intensity of dust. Examining the correlation between Ilam dust and the identified sources showed that the regression model for Ilam city was most strongly associated with the Wasit region, with a correlation coefficient of 82.96%.Introduction Dust storms are among the most significant atmospheric hazards in arid and semi-arid regions, often causing substantial environmental and socio-economic damage. This phenomenon is particularly prevalent along the global dust belt, stretching from the western coasts of North Africa to the Middle East. Dust particles suspended in the atmosphere may originate from natural sources such as soil erosion and volcanic activity, as well as from anthropogenic pollutants. In western Iran, especially in Ilam Province, dust storms primarily originate in neighboring Iraq. Due to the region’s climatic conditions and lack of adequate infrastructure, Ilam is highly vulnerable to frequent and intense dust storm events. Recent research has demonstrated the growing application of remote sensing data and geospatial information systems (GIS) for monitoring and analyzing dust phenomena across Iran. In this context, several studies have focused on the spatial and temporal analysis of dust events in various parts of the country. The present study specifically investigates the spatiotemporal variability of dust storms in Ilam County. Utilizing MODIS satellite imagery and tensor analysis tools within the Google Earth Engine (GEE) platform, this research aims to assess dust distribution patterns over time and space. The findings identify primary dust sources and characterize the temporal evolution and spatial extent of dust activity in the region, resulting in the development of detailed spatiotemporal dust distribution maps for Ilam. This section outlines the study area, datasets, proposed methods, and data preprocessing steps undertaken for the analysis. Ilam County is located in the northwest of Ilam Province, Iran. It is bordered by Eyvan County to the north, Sirvan to the east, Mehran to the south, and Iraq to the west. The region features a temperate mountainous climate, with an average annual precipitation of approximately 619 millimeters and absolute temperature extremes ranging from -13°C to 41°C. Geographically, Ilam County holds strategic significance due to its shared border with Iraq, a primary contributor to dust generation in the Middle East. Given the transboundary nature of dust transport in this region, the spatial extent of the study area was expanded to include zones adjacent to the Iraqi border. This allowed for a more comprehensive analysis of the spatiotemporal distribution of dust storms affecting Ilam.Material and MethodsSatellite Data and Processing WorkflowThis study used satellite data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, which is mounted on NASA's Terra and Aqua satellites for global atmospheric monitoring. MODIS features 36 spectral bands with varying spatial resolutions. For the retrieval of dust-related parameters, the Deep Blue (DB) algorithm was employed to extract the Aerosol Optical Depth (AOD) index, which quantifies the concentration of airborne particulate matter. The primary dataset used in this research is the MCD19A2 daily AOD product, originally provided at a spatial resolution of 1 kilometer. For analytical purposes, the data were resampled to a 2-kilometer resolution to optimize computational efficiency. Dust storm days were identified and extracted within the Google Earth Engine (GEE) platform, and further processing and visualization were conducted using MATLAB software.Google Earth EngineGoogle Earth Engine (GEE) is a web-based cloud processing platform launched by Google in 2010, providing free and rapid access to remote sensing data. As one of the largest platforms for spatial data analysis, GEE is widely used in climate studies, meteorology, and remote sensing applications. Its use provides significant time, cost, and human resource savings, as well as enhanced accuracy in studies due to access to up-to-date and comprehensive data repositories.Tensor AnalysisTo simultaneously analyze spatial and temporal phenomena, multidimensional structures are required, which are provided by tensor analysis. In this study, a third-order (three-dimensional) tensor was employed in the MATLAB environment to model and analyze the data based on geographic latitude, longitude, and time. Unlike traditional statistical methods, the use of tensors enables the examination of complex spatiotemporal phenomena with greater precision and differentiation. Dust storms are not only a nuisance but also have profound environmental and human consequences. Identifying the origins of this phenomenon, particularly in the border regions of western Iran, such as Ilam County, is of critical importance. The majority of these dust storms originate from the deserts of Iraq, Saudi Arabia, and Syria, where poor water management practices and hydraulic construction projects have contributed to land degradation, making it more susceptible to dust production. In this study, we used remote sensing technology and the Google Earth Engine platform to investigate daily dust storm variations during the spring of 2022. Unlike previous studies that primarily focused on periodic or seasonal analyses, we employed daily MODIS satellite data in conjunction with tensor analysis to provide a more precise and visual representation of dust storm events. The tensor-based plots for March revealed two significant dust waves on the 4th and 6th of March, originating from the Wasit region of Iraq, which impacted Ilam County and the Maysan region. The intensity of the first wave was such that horizontal visibility in Ilam dropped to just 800 meters. The second wave, although less intense, was still notably impactful. In April, three distinct dust waves affected Ilam, originating from the Balad and Mesopotamian regions. These storms occasionally reduced horizontal visibility to as low as 500 meters. However, the most severe dust events occurred in May, with five consecutive waves originating from the Al Anbar, Wasit, and Maysan regions. On May 23, visibility in Ilam dropped to zero, marking the worst conditions during the study period. June also saw two waves originating from Balad and Maysan, with visibility in Ilam reduced to 2000 meters. The analysis showed that May recorded the highest Aerosol Optical Depth (AOD) values, indicating the greatest dust density.Validation of ResultsTo validate our findings, the AOD data were cross-verified with ground station measurements from the source regions and Ilam. Specifically, a strong correlation was found between AOD variations in the Wasit region of Iraq and Ilam County. Regression modeling confirmed this correlation, indicating that whenever dust storms occur in the Wasit region, Ilam County is likely to be affected as well.ConclusionThis study, utilizing spatiotemporal tensor analysis, successfully identified the origin and daily pattern of dust storms in Ilam County from March to June 2022. The data used in this research were extracted from the MODIS sensor (MCD19A2 product), and dust storm days were selected based on reports from the Meteorological Organization. A key advantage of applying tensor analysis in this study is its ability to simultaneously represent spatial and temporal variations of dust storms in a three-dimensional space, a feature not available in traditional two-dimensional methods, such as satellite imagery. This approach allowed for a precise examination of the dust storm origins on a daily basis, marking an innovative step in atmospheric monitoring. The results of the analysis revealed that the dust storm situation in the study area was critical. Four major regions were identified as the primary sources of dust: Balad, Mesopotamia, Maysan, and Wasit. Among these sources, the Mesopotamian region played a dominant role, particularly in May, with an AOD index of 3.85, indicating the highest frequency and intensity of dust storms. Furthermore, correlation matrix analysis showed the most direct and significant relationship between dust storms in Ilam County and the Wasit region of Iraq. This study demonstrated that tensor analysis is a powerful tool for monitoring and forecasting dust storm behavior, offering valuable support for environmental policymakers and disaster management authorities in making informed, scientific decisions. It is recommended that future studies incorporate additional variables such as temperature, vegetation cover, wind patterns, and climate change to develop more comprehensive models for dust storm monitoring.One of the main factors contributing to air pollution is the phenomenon of dust, which causes significant damage to various economic, social, and human resources. Dust events occur in different parts of the world, especially in arid and semi-arid regions, and are driven by both natural and human factors. This research aimed to identify the origin of dust in Ilam city using the spatio-temporal tensor of aerosol optical depth (AOD) derived from MODIS sensor data during the period March–June 2022. First, dusty days were extracted from meteorological data, and the spatio-temporal tensor of aerosol optical depth was generated. The reason for using the tensor was to examine large volumes of data across both spatial and temporal dimensions simultaneously. The results of comparing the tensor with corresponding meteorological data showed that whenever the aerosol optical depth exceeded 0.5, dust events occurred. The spatio-temporal dust tensor analysis revealed that dust levels are directly related to wind speed, and that dust events occur when wind speed exceeds 15 m/s. Finally, by identifying the spatial variations in AOD, four dust sources were detected in the study area (Beld, Mesopotamia, Misan, and Wasit), with Mesopotamia recognized as a major potential source. The analysis of the temporal pattern of AOD indicated an increasing trend in May. The highest AOD value, 3.85 in May, reflected the intensity of dust. Examining the correlation between Ilam dust and the identified sources showed that the regression model for Ilam city was most strongly associated with the Wasit region, with a correlation coefficient of 82.96%.Introduction Dust storms are among the most significant atmospheric hazards in arid and semi-arid regions, often causing substantial environmental and socio-economic damage. This phenomenon is particularly prevalent along the global dust belt, stretching from the western coasts of North Africa to the Middle East. Dust particles suspended in the atmosphere may originate from natural sources such as soil erosion and volcanic activity, as well as from anthropogenic pollutants. In western Iran, especially in Ilam Province, dust storms primarily originate in neighboring Iraq. Due to the region’s climatic conditions and lack of adequate infrastructure, Ilam is highly vulnerable to frequent and intense dust storm events. Recent research has demonstrated the growing application of remote sensing data and geospatial information systems (GIS) for monitoring and analyzing dust phenomena across Iran. In this context, several studies have focused on the spatial and temporal analysis of dust events in various parts of the country. The present study specifically investigates the spatiotemporal variability of dust storms in Ilam County. Utilizing MODIS satellite imagery and tensor analysis tools within the Google Earth Engine (GEE) platform, this research aims to assess dust distribution patterns over time and space. The findings identify primary dust sources and characterize the temporal evolution and spatial extent of dust activity in the region, resulting in the development of detailed spatiotemporal dust distribution maps for Ilam. This section outlines the study area, datasets, proposed methods, and data preprocessing steps undertaken for the analysis. Ilam County is located in the northwest of Ilam Province, Iran. It is bordered by Eyvan County to the north, Sirvan to the east, Mehran to the south, and Iraq to the west. The region features a temperate mountainous climate, with an average annual precipitation of approximately 619 millimeters and absolute temperature extremes ranging from -13°C to 41°C. Geographically, Ilam County holds strategic significance due to its shared border with Iraq, a primary contributor to dust generation in the Middle East. Given the transboundary nature of dust transport in this region, the spatial extent of the study area was expanded to include zones adjacent to the Iraqi border. This allowed for a more comprehensive analysis of the spatiotemporal distribution of dust storms affecting Ilam.Material and MethodsSatellite Data and Processing WorkflowThis study used satellite data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, which is mounted on NASA's Terra and Aqua satellites for global atmospheric monitoring. MODIS features 36 spectral bands with varying spatial resolutions. For the retrieval of dust-related parameters, the Deep Blue (DB) algorithm was employed to extract the Aerosol Optical Depth (AOD) index, which quantifies the concentration of airborne particulate matter. The primary dataset used in this research is the MCD19A2 daily AOD product, originally provided at a spatial resolution of 1 kilometer. For analytical purposes, the data were resampled to a 2-kilometer resolution to optimize computational efficiency. Dust storm days were identified and extracted within the Google Earth Engine (GEE) platform, and further processing and visualization were conducted using MATLAB software.Google Earth EngineGoogle Earth Engine (GEE) is a web-based cloud processing platform launched by Google in 2010, providing free and rapid access to remote sensing data. As one of the largest platforms for spatial data analysis, GEE is widely used in climate studies, meteorology, and remote sensing applications. Its use provides significant time, cost, and human resource savings, as well as enhanced accuracy in studies due to access to up-to-date and comprehensive data repositories.Tensor AnalysisTo simultaneously analyze spatial and temporal phenomena, multidimensional structures are required, which are provided by tensor analysis. In this study, a third-order (three-dimensional) tensor was employed in the MATLAB environment to model and analyze the data based on geographic latitude, longitude, and time. Unlike traditional statistical methods, the use of tensors enables the examination of complex spatiotemporal phenomena with greater precision and differentiation. Dust storms are not only a nuisance but also have profound environmental and human consequences. Identifying the origins of this phenomenon, particularly in the border regions of western Iran, such as Ilam County, is of critical importance. The majority of these dust storms originate from the deserts of Iraq, Saudi Arabia, and Syria, where poor water management practices and hydraulic construction projects have contributed to land degradation, making it more susceptible to dust production. In this study, we used remote sensing technology and the Google Earth Engine platform to investigate daily dust storm variations during the spring of 2022. Unlike previous studies that primarily focused on periodic or seasonal analyses, we employed daily MODIS satellite data in conjunction with tensor analysis to provide a more precise and visual representation of dust storm events. The tensor-based plots for March revealed two significant dust waves on the 4th and 6th of March, originating from the Wasit region of Iraq, which impacted Ilam County and the Maysan region. The intensity of the first wave was such that horizontal visibility in Ilam dropped to just 800 meters. The second wave, although less intense, was still notably impactful. In April, three distinct dust waves affected Ilam, originating from the Balad and Mesopotamian regions. These storms occasionally reduced horizontal visibility to as low as 500 meters. However, the most severe dust events occurred in May, with five consecutive waves originating from the Al Anbar, Wasit, and Maysan regions. On May 23, visibility in Ilam dropped to zero, marking the worst conditions during the study period. June also saw two waves originating from Balad and Maysan, with visibility in Ilam reduced to 2000 meters. The analysis showed that May recorded the highest Aerosol Optical Depth (AOD) values, indicating the greatest dust density.Validation of ResultsTo validate our findings, the AOD data were cross-verified with ground station measurements from the source regions and Ilam. Specifically, a strong correlation was found between AOD variations in the Wasit region of Iraq and Ilam County. Regression modeling confirmed this correlation, indicating that whenever dust storms occur in the Wasit region, Ilam County is likely to be affected as well.ConclusionThis study, utilizing spatiotemporal tensor analysis, successfully identified the origin and daily pattern of dust storms in Ilam County from March to June 2022. The data used in this research were extracted from the MODIS sensor (MCD19A2 product), and dust storm days were selected based on reports from the Meteorological Organization. A key advantage of applying tensor analysis in this study is its ability to simultaneously represent spatial and temporal variations of dust storms in a three-dimensional space, a feature not available in traditional two-dimensional methods, such as satellite imagery. This approach allowed for a precise examination of the dust storm origins on a daily basis, marking an innovative step in atmospheric monitoring. The results of the analysis revealed that the dust storm situation in the study area was critical. Four major regions were identified as the primary sources of dust: Balad, Mesopotamia, Maysan, and Wasit. Among these sources, the Mesopotamian region played a dominant role, particularly in May, with an AOD index of 3.85, indicating the highest frequency and intensity of dust storms. Furthermore, correlation matrix analysis showed the most direct and significant relationship between dust storms in Ilam County and the Wasit region of Iraq. This study demonstrated that tensor analysis is a powerful tool for monitoring and forecasting dust storm behavior, offering valuable support for environmental policymakers and disaster management authorities in making informed, scientific decisions. It is recommended that future studies incorporate additional variables such as temperature, vegetation cover, wind patterns, and climate change to develop more comprehensive models for dust storm monitoring.https://geoeh.um.ac.ir/article_44419_eee683c03a8c960e1309ce1cb5523a1e.pdfFerdowsi University of MashhadJournal of Geography and Environmental Hazards2322-168214320250923Analysis of the Effects of Tectonic Processes on Geomorphological Features and Assessment of Slope Instability Hazard Potential in the Shaharchay River Basin of UrmiaAnalysis of the Effects of Tectonic Processes on Geomorphological Features and Assessment of Slope Instability Hazard Potential in the Shaharchay River Basin of Urmia18374709310.22067/geoeh.2025.93808.1578FAFaribaHemmatiDepartment of Geography Education, Farhangian University,Tehran,Iran0000-0003-4023-8614SaberSeyyed AhmadiMaster of Science, Technical and Soil Mechanics Laboratory, Maku, Iran0009-0009-5980-1316AkramAlizadehAssociate Professor, Department of Geology, Faculty of Sciences, Urmia University, Urmia, Iran0000-0003-3174-8786Journal Article20250604This study was conducted to analyze the effects of active tectonic processes on geomorphological features and assess the potential for slope instability occurrences within the Shaharchay River Basin of Urmia. To achieve this objective, an integrated approach was employed, utilizing morphotectonic indices (such as hierarchical stream anomaly, anomaly density, and bifurcation ratio), logistic regression statistical modeling, and analysis of remote sensing data and field data. The results from the logistic regression model (with an ROC accuracy of 0.922) indicate that approximately 43.13% of recorded instabilities in areas characterized by semi-resistant to resistant lithologies are located within less than 1 km from active faults. Furthermore, 87.25% of these instabilities occurred within 3 km of rivers, and occurred within 2 km of roads 90%, typically in areas with sparse or inadequate vegetation cover. The analysis of landslide hazard zonation reveals that the highest hazard is concentrated in elevations ranging 1268 and 2000 m and on south-facing slopes. The findings highlight the significant role of active tectonics, slope gradient, and human infrastructure (such as roads) in intensifying slope instability processes. Consequently, adopting an integrated approach to risk management, restoring natural vegetation cover, and implementing land-use restrictions in high-risk areas are deemed essential.IntroductionLandslide hazards and slope instabilities rank among the most devastating geomorphological natural hazards, inflicting significant human, economic, and environmental losses on a global scale each year. Iran, situated within the tectonically active Alpine-Himalayan belt, is especially prone to these hazards due to its rugged mountainous terrain, seasonal heavy rainfall, and complex geological framework. This study focuses on the Shahrchay River Basin in Urmia, northwestern Iran, a region characterized by intense tectonic activity and diverse geomorphological features that amplify the risk of slope failures. The historical backdrop reveals that landslides in Iran, particularly in mountainous zones such as the Zagros, Alborz, and Binalud ranges, are driven by a complex interplay of natural factors—including geological composition, topographic steepness, climatic variability, and hydrological dynamics—and human-induced activities such as land-use alterations, road construction, and vegetation clearance. Previous research, including works by Hemmati and Mokhtari (2017) and Baharvand and Sori (2015), has underscored the pivotal role of tectonic processes, notably faulting and the resultant increase in shear stress, in initiating landslides. The Shahrchay Basin, located near active fault systems like the Urmia Lake Fault, North and South Salmas Faults, Ashnoye Fault, and Kuh-e-Shahidan Fault, exemplifies this vulnerability, with notable seismic events such as the 5.7 magnitude Silvaneh earthquake in April 2015, the 7.9 magnitude earthquake in October 1965, and the aftershocks following the 1982 Ahar-Varzeqan earthquake highlighting the region’s seismic susceptibility. The primary objective of this research is to explore the detailed relationship between morphotectonic indices, drainage network patterns, and slope instability risks, tackling the core question of how active tectonic processes reshape drainage structures and predispose the area to landslides. This study addresses a significant knowledge gap, as integrated analyses combining tectonic, geomorphological, and anthropogenic factors in the Shahrchay Basin remain scarce, despite the urgent need for enhanced risk management, hazard zoning, and sustainable development strategies. The research is directed toward environmental scientists, geologists, urban planners, policymakers, and disaster management professionals, employing a multidisciplinary approach that integrates geomorphological assessments with advanced statistical modeling to deliver practical insights for land-use planning and disaster mitigation. The methodological design is grounded in its ability to construct a theoretical framework for understanding tectonic influences on slope stability while providing actionable applications for hazard reduction in comparable mountainous regions. The literature review, drawing on studies by Khazri et al. (2006), Nikjoo et al. (2016), and Rofii et al. (2014), highlights ongoing debates regarding the relative impacts of tectonic versus human factors, leading to the hypothesis that tectonic activity, when compounded by human disturbances, is a primary driver of landslide occurrences in the basin. This framing sets expectations for a comprehensive analysis that bridges theoretical geomorphology with practical policy implications, addressing both the scientific community’s need for deeper understanding and the public’s demand for safety measures.Material and Methods This investigation adopts a multi-stage, integrated methodology to evaluate the contributions of geomorphological and tectonic factors to slope instabilities in the Shahrchay River Basin. The approach encompasses data collection, spatial processing using Geographic Information Systems (GIS), extraction of geomorphic indices, statistical modeling, and thorough validation of findings. Data were compiled from diverse sources: satellite imagery from Landsat-9 (2023, 30-meter resolution) and Sentinel-2 (10-meter resolution) supplied land cover and Digital Elevation Model (DEM) data, obtained from the USGS, while geological maps (1:100,000 scale) and topographic maps (1:50,000 scale) were sourced from the Geological Survey of Iran and the National Cartographic Center, respectively. Field surveys augmented these datasets with detailed records of landslide locations, types, and characteristics, ensuring a robust empirical base. In the GIS environment, topographic layers-such as slope, aspect, and elevation classes—were generated from the DEM, and landslide distribution was mapped using Band 6 of Landsat-9 imagery, correlated with nine critical variables: elevation, slope, aspect, distance from faults, rivers, roads, lithology, vegetation cover, and land use. Each variable was assigned a sensitivity score ranging from 1 to 5, with 5 indicating the highest risk, following established geohazard assessment protocols. Key geomorphic indices, including hierarchical anomaly (Δa), anomaly density (ga), bifurcation ratio (Rb), asymmetry factor (AF), and basin shape ratio, were calculated using standardized formulas within GIS, building on methodologies outlined by Nikjoo et al. (2016) and Burbank and Anderson (2011). The logistic regression model was applied to predict landslide probability, utilizing a nonlinear S-shaped curve to link independent variables (e.g., distance from faults) with a binary dependent variable (landslide occurrence or non-occurrence). Calibration involved assigning 1 to unstable areas and 0 to stable ones, with data processed in IDRISI software. Validation was performed by comparing zoning maps with field observations, employing Chi-Square tests (95% confidence level), Receiver Operating Characteristic (ROC) curves (target value >0.7), and Pseudo R² (>0.2) to assess model accuracy, fit, and predictive power. This methodology ensures replicability and complies with ethical standards by relying on secondary data and non-invasive field techniques, with no direct involvement of human or animal subjects, thus negating the need for specific consent procedures. The approach’s descriptive and interpretive nature aligns with the study’s objectives of elucidating tectonic impacts and informing practical interventions.Results and Discussion Analysis reveals a multifaceted interaction between tectonic processes and geomorphological dynamics in driving slope instabilities throughout the Shahrchay Basin. The hierarchical anomaly index (Δa) of 1.34 indicates a lower-than-expected ratio of higher-order to first-order streams, suggesting potential tectonic disruption or geomorphic adjustments, a pattern supported by regional studies from Khazri et al. (2006). The anomaly density (ga) of 1.33 anomalies per square kilometer reflects significant tectonic activity, while the bifurcation ratio (Rb) of 1.75, below the global average of 3-5, may suggest lithological or climatic modulation rather than dominant tectonic control, consistent with insights from Burbank and Anderson (2011). The asymmetry factor (AF) of 54.39 indicates a leftward stream shift, likely attributable to lateral fault movements, and a basin shape ratio of 5.62 suggests tectonic elongation aligned with fault orientations. Spatial analysis of landslide distribution, derived from 2023 Landsat imagery, identified 42.4 square kilometers (0.61% of the basin) as unstable, with a pronounced concentration in lower elevations (1268-2000 meters) where human activities such as agriculture, road construction, and urbanization are prevalent, resulting in reduced vegetation cover and diminished soil cohesion. Slope analysis indicated peak instability at 5-20% gradients, linked to human disturbances and unstable lithologies like marls, shales, and loose alluvial deposits, while southern and southeastern aspects exhibited higher risks due to increased solar exposure and soil desiccation.This study employed a logistic regression model (ROC = 0.922) to analyze the key factors influencing slope instability. The results indicated that elevation, slope, distance to faults and rivers, and vegetation cover type exerted the most significant influence, respectively. The findings revealed that 87.25% of instability events occurred within 3 km of rivers, 90% occurred within 2 km of roads in areas of sparse vegetation, and 43.13% occurred in moderate to resistant lithological units within 1 km of active faults. Spatial analyses further showed that hazard hotspots were primarily located at elevations of 1268–2000 m, on gentle to moderate slopes (5–20%), and on southern and southeastern aspects. Moreover, over 87% of instability events occurred within 3000 meters of watercourses. The results underscore the determining tripartite influence of active tectonics (faults), topography (elevation/slope), and anthropogenic activities (road construction/vegetation reduction) in exacerbating geohazards.Conclusion This research illuminates the dominant role of tectonic activities, particularly active faulting, in shaping slope instability hazards within the Shahrchay Basin, with morphotectonic indices providing crucial insights into drainage network disruptions and landslide triggers. The findings reaffirm the importance of the research problem, as landslides in tectonically active regions like Iran continue to endanger human lives, infrastructure, and ecosystems, a concern echoed by Hemmati and Mokhtari (2017), Ngarash et al. (2013), and Baharvand and Sori (2015). Theoretically, the study enriches geomorphological models by integrating tectonic and anthropogenic influences, offering a foundation for future research into hazard dynamics and landscape evolution. Practically, the results provide critical guidance for land-use policies, advocating for restricted development in high-risk zones, prioritized reforestation to enhance slope stability, and improved water management to mitigate erosion. The high ROC value (0.9223) and field validation indicate that the logistic regression model accurately predicts landslide susceptibility, potentially explaining real-world phenomena such as seasonal landslide reactivation during heavy rains, as observed by Baharvand and Sori (2015), and offering a basis for modeling similar geohazards elsewhere. Applications are justified, particularly in zoning for controlled construction, implementing watershed management projects, and developing early warning systems, yet unresolved challenges persist, including accounting for long-term climatic variability, adapting to urban expansion pressures, and refining models with advanced technologies like LiDAR and real-time monitoring. The study’s contribution justifies attention from both specialists and policymakers, offering a replicable framework for hazard management, which can be extended to other tectonically active basins in Iran, addressing broader environmental resilience, disaster preparedness, and sustainable development issues. Future research could focus on longitudinal studies to track tectonic and climatic interactions, enhancing predictive models and supporting adaptive management strategies in the face of evolving environmental conditions.This study was conducted to analyze the effects of active tectonic processes on geomorphological features and assess the potential for slope instability occurrences within the Shaharchay River Basin of Urmia. To achieve this objective, an integrated approach was employed, utilizing morphotectonic indices (such as hierarchical stream anomaly, anomaly density, and bifurcation ratio), logistic regression statistical modeling, and analysis of remote sensing data and field data. The results from the logistic regression model (with an ROC accuracy of 0.922) indicate that approximately 43.13% of recorded instabilities in areas characterized by semi-resistant to resistant lithologies are located within less than 1 km from active faults. Furthermore, 87.25% of these instabilities occurred within 3 km of rivers, and occurred within 2 km of roads 90%, typically in areas with sparse or inadequate vegetation cover. The analysis of landslide hazard zonation reveals that the highest hazard is concentrated in elevations ranging 1268 and 2000 m and on south-facing slopes. The findings highlight the significant role of active tectonics, slope gradient, and human infrastructure (such as roads) in intensifying slope instability processes. Consequently, adopting an integrated approach to risk management, restoring natural vegetation cover, and implementing land-use restrictions in high-risk areas are deemed essential.IntroductionLandslide hazards and slope instabilities rank among the most devastating geomorphological natural hazards, inflicting significant human, economic, and environmental losses on a global scale each year. Iran, situated within the tectonically active Alpine-Himalayan belt, is especially prone to these hazards due to its rugged mountainous terrain, seasonal heavy rainfall, and complex geological framework. This study focuses on the Shahrchay River Basin in Urmia, northwestern Iran, a region characterized by intense tectonic activity and diverse geomorphological features that amplify the risk of slope failures. The historical backdrop reveals that landslides in Iran, particularly in mountainous zones such as the Zagros, Alborz, and Binalud ranges, are driven by a complex interplay of natural factors—including geological composition, topographic steepness, climatic variability, and hydrological dynamics—and human-induced activities such as land-use alterations, road construction, and vegetation clearance. Previous research, including works by Hemmati and Mokhtari (2017) and Baharvand and Sori (2015), has underscored the pivotal role of tectonic processes, notably faulting and the resultant increase in shear stress, in initiating landslides. The Shahrchay Basin, located near active fault systems like the Urmia Lake Fault, North and South Salmas Faults, Ashnoye Fault, and Kuh-e-Shahidan Fault, exemplifies this vulnerability, with notable seismic events such as the 5.7 magnitude Silvaneh earthquake in April 2015, the 7.9 magnitude earthquake in October 1965, and the aftershocks following the 1982 Ahar-Varzeqan earthquake highlighting the region’s seismic susceptibility. The primary objective of this research is to explore the detailed relationship between morphotectonic indices, drainage network patterns, and slope instability risks, tackling the core question of how active tectonic processes reshape drainage structures and predispose the area to landslides. This study addresses a significant knowledge gap, as integrated analyses combining tectonic, geomorphological, and anthropogenic factors in the Shahrchay Basin remain scarce, despite the urgent need for enhanced risk management, hazard zoning, and sustainable development strategies. The research is directed toward environmental scientists, geologists, urban planners, policymakers, and disaster management professionals, employing a multidisciplinary approach that integrates geomorphological assessments with advanced statistical modeling to deliver practical insights for land-use planning and disaster mitigation. The methodological design is grounded in its ability to construct a theoretical framework for understanding tectonic influences on slope stability while providing actionable applications for hazard reduction in comparable mountainous regions. The literature review, drawing on studies by Khazri et al. (2006), Nikjoo et al. (2016), and Rofii et al. (2014), highlights ongoing debates regarding the relative impacts of tectonic versus human factors, leading to the hypothesis that tectonic activity, when compounded by human disturbances, is a primary driver of landslide occurrences in the basin. This framing sets expectations for a comprehensive analysis that bridges theoretical geomorphology with practical policy implications, addressing both the scientific community’s need for deeper understanding and the public’s demand for safety measures.Material and Methods This investigation adopts a multi-stage, integrated methodology to evaluate the contributions of geomorphological and tectonic factors to slope instabilities in the Shahrchay River Basin. The approach encompasses data collection, spatial processing using Geographic Information Systems (GIS), extraction of geomorphic indices, statistical modeling, and thorough validation of findings. Data were compiled from diverse sources: satellite imagery from Landsat-9 (2023, 30-meter resolution) and Sentinel-2 (10-meter resolution) supplied land cover and Digital Elevation Model (DEM) data, obtained from the USGS, while geological maps (1:100,000 scale) and topographic maps (1:50,000 scale) were sourced from the Geological Survey of Iran and the National Cartographic Center, respectively. Field surveys augmented these datasets with detailed records of landslide locations, types, and characteristics, ensuring a robust empirical base. In the GIS environment, topographic layers-such as slope, aspect, and elevation classes—were generated from the DEM, and landslide distribution was mapped using Band 6 of Landsat-9 imagery, correlated with nine critical variables: elevation, slope, aspect, distance from faults, rivers, roads, lithology, vegetation cover, and land use. Each variable was assigned a sensitivity score ranging from 1 to 5, with 5 indicating the highest risk, following established geohazard assessment protocols. Key geomorphic indices, including hierarchical anomaly (Δa), anomaly density (ga), bifurcation ratio (Rb), asymmetry factor (AF), and basin shape ratio, were calculated using standardized formulas within GIS, building on methodologies outlined by Nikjoo et al. (2016) and Burbank and Anderson (2011). The logistic regression model was applied to predict landslide probability, utilizing a nonlinear S-shaped curve to link independent variables (e.g., distance from faults) with a binary dependent variable (landslide occurrence or non-occurrence). Calibration involved assigning 1 to unstable areas and 0 to stable ones, with data processed in IDRISI software. Validation was performed by comparing zoning maps with field observations, employing Chi-Square tests (95% confidence level), Receiver Operating Characteristic (ROC) curves (target value >0.7), and Pseudo R² (>0.2) to assess model accuracy, fit, and predictive power. This methodology ensures replicability and complies with ethical standards by relying on secondary data and non-invasive field techniques, with no direct involvement of human or animal subjects, thus negating the need for specific consent procedures. The approach’s descriptive and interpretive nature aligns with the study’s objectives of elucidating tectonic impacts and informing practical interventions.Results and Discussion Analysis reveals a multifaceted interaction between tectonic processes and geomorphological dynamics in driving slope instabilities throughout the Shahrchay Basin. The hierarchical anomaly index (Δa) of 1.34 indicates a lower-than-expected ratio of higher-order to first-order streams, suggesting potential tectonic disruption or geomorphic adjustments, a pattern supported by regional studies from Khazri et al. (2006). The anomaly density (ga) of 1.33 anomalies per square kilometer reflects significant tectonic activity, while the bifurcation ratio (Rb) of 1.75, below the global average of 3-5, may suggest lithological or climatic modulation rather than dominant tectonic control, consistent with insights from Burbank and Anderson (2011). The asymmetry factor (AF) of 54.39 indicates a leftward stream shift, likely attributable to lateral fault movements, and a basin shape ratio of 5.62 suggests tectonic elongation aligned with fault orientations. Spatial analysis of landslide distribution, derived from 2023 Landsat imagery, identified 42.4 square kilometers (0.61% of the basin) as unstable, with a pronounced concentration in lower elevations (1268-2000 meters) where human activities such as agriculture, road construction, and urbanization are prevalent, resulting in reduced vegetation cover and diminished soil cohesion. Slope analysis indicated peak instability at 5-20% gradients, linked to human disturbances and unstable lithologies like marls, shales, and loose alluvial deposits, while southern and southeastern aspects exhibited higher risks due to increased solar exposure and soil desiccation.This study employed a logistic regression model (ROC = 0.922) to analyze the key factors influencing slope instability. The results indicated that elevation, slope, distance to faults and rivers, and vegetation cover type exerted the most significant influence, respectively. The findings revealed that 87.25% of instability events occurred within 3 km of rivers, 90% occurred within 2 km of roads in areas of sparse vegetation, and 43.13% occurred in moderate to resistant lithological units within 1 km of active faults. Spatial analyses further showed that hazard hotspots were primarily located at elevations of 1268–2000 m, on gentle to moderate slopes (5–20%), and on southern and southeastern aspects. Moreover, over 87% of instability events occurred within 3000 meters of watercourses. The results underscore the determining tripartite influence of active tectonics (faults), topography (elevation/slope), and anthropogenic activities (road construction/vegetation reduction) in exacerbating geohazards.Conclusion This research illuminates the dominant role of tectonic activities, particularly active faulting, in shaping slope instability hazards within the Shahrchay Basin, with morphotectonic indices providing crucial insights into drainage network disruptions and landslide triggers. The findings reaffirm the importance of the research problem, as landslides in tectonically active regions like Iran continue to endanger human lives, infrastructure, and ecosystems, a concern echoed by Hemmati and Mokhtari (2017), Ngarash et al. (2013), and Baharvand and Sori (2015). Theoretically, the study enriches geomorphological models by integrating tectonic and anthropogenic influences, offering a foundation for future research into hazard dynamics and landscape evolution. Practically, the results provide critical guidance for land-use policies, advocating for restricted development in high-risk zones, prioritized reforestation to enhance slope stability, and improved water management to mitigate erosion. The high ROC value (0.9223) and field validation indicate that the logistic regression model accurately predicts landslide susceptibility, potentially explaining real-world phenomena such as seasonal landslide reactivation during heavy rains, as observed by Baharvand and Sori (2015), and offering a basis for modeling similar geohazards elsewhere. Applications are justified, particularly in zoning for controlled construction, implementing watershed management projects, and developing early warning systems, yet unresolved challenges persist, including accounting for long-term climatic variability, adapting to urban expansion pressures, and refining models with advanced technologies like LiDAR and real-time monitoring. The study’s contribution justifies attention from both specialists and policymakers, offering a replicable framework for hazard management, which can be extended to other tectonically active basins in Iran, addressing broader environmental resilience, disaster preparedness, and sustainable development issues. Future research could focus on longitudinal studies to track tectonic and climatic interactions, enhancing predictive models and supporting adaptive management strategies in the face of evolving environmental conditions.https://geoeh.um.ac.ir/article_47093_7fe79c97dac8df04ef7f328137ffdec8.pdfFerdowsi University of MashhadJournal of Geography and Environmental Hazards2322-168214320250923Modeling Landslide Susceptibility Using Artificial Neural Network Algorithm: A Case Study of Shahid Abbaspour Dam Basin, Northeast KhuzestanModeling Landslide Susceptibility Using Artificial Neural Network Algorithm: A Case Study of Shahid Abbaspour Dam Basin, Northeast Khuzestan38544689510.22067/geoeh.2025.92634.1557FAMahshidMoaviDepartment of Physical Geography, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran0009-0006-6947-4505HeevaElmizadehDepartment of Marine Geology, Faculty of Marine Natural Resources, Khorramshahr University of Marine Science and Technology, Khorramshahr, Iran0000-0002-5096-0603MojganEntezariDepartment of Physical Geography, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran0000-0003-0014-0209Journal Article20250326Landslides are natural hazards that pose significant risks to human lives and the environment. Landslide susceptibility maps are vital tools for planning, management, and risk-reduction strategies. Various methods exist for generating these maps, but their effectiveness varies across studies. This research aimed to develop a landslide susceptibility map for the Shahid Abbaspour Dam watershed using an Artificial Neural Network (ANN). Landslide data points were randomly split into a 70% training and 30% testing dataset. Fifteen influencing factors were selected as model inputs, including elevation, slope, aspect, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), distance to roads and rivers, geology, soil texture, land use, and precipitation. Model validation was performed using accuracy metrics and statistical indices (RMSE, Cohen’s Kappa, MAE, and R²). The ANN model demonstrated strong predictive performance, achieving an accuracy of 0.8543. The study identified rivers as the most critical factor influencing landslide susceptibility in the area, followed by slope and geological formations. The highest susceptibility zones were found in the southwest and in a small northern section of the watershed. The resulting map provides valuable support for land-use planning and sustainable development strategies aimed at mitigating landslide risks in the region.Introduction Landslides rank among the most destructive natural hazards, endangering human lives, infrastructure, and the natural environment across the globe. In Iran, the northeastern part of Khuzestan Province, including the Shahid Abbaspour Dam watershed, is especially prone to landslides due to its mountainous topography, diverse geological formations, and seasonal rainfall patterns. Landslide susceptibility maps, which estimate the likelihood of occurrence based on local terrain and environmental conditions, are critical tools for planners, engineers, and policymakers. These maps guide land-use decisions, infrastructure development, and disaster preparedness, thereby reducing potential impacts on communities and ecosystems. Various methods, ranging from statistical models to machine learning techniques, have been employed to create such maps, each with varying degrees of accuracy and applicability. However, few studies have applied advanced machine learning approaches, such as Artificial Neural Networks (ANNs), in the Shahid Abbaspour Dam watershed, a critical area within the Greater Karun watershed. This research fills this gap by modeling landslide susceptibility in the region using an ANN, a method known for its ability to handle complex, non-linear relationships among multiple variables. The objective of this study is to produce a reliable susceptibility map to support risk mitigation, land-use planning, and sustainable development in this environmentally sensitive area. By leveraging the ANN’s predictive power, this study offers a robust, scientifically grounded approach to addressing a pressing regional challenge, contributing to the broader field of geohazard assessment.Material and MethodsThe study focuses on the Shahid Abbaspour Dam watershed, located in northeastern Khuzestan. This area, part of the Greater Karun watershed, features rugged terrain, diverse geology, and a history of landslide activity, making it ideal for susceptibility analysis. Our methodology was conducted in five comprehensive stages to assess the probability of landslides based on local conditions. First, we conducted an extensive literature review to identify relevant factors and gathered data from multiple sources. Second, we selected and prepared 15 influencing factors as spatial layers: elevation, slope gradient, slope aspect, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), distance from roads, distance from rivers, proximity to faults, geological formations, soil texture, land-use patterns, precipitation, Normalized Difference Vegetation Index (NDVI), and seismic activity. These datasets were derived from 1:50,000-scale topographic maps, 1:100,000-scale geological maps, and a 30-meter resolution Digital Elevation Model (DEM) from ASTER satellite imagery. In the third stage, we implemented an Artificial Neural Network (ANN) in the R programming environment using the nnet package. This machine learning approach excels at capturing non-linear patterns among variables, making it suitable for landslide prediction. The ANN was configured with a single hidden layer of 10 neurons, trained with 70% of the dataset, and tested with the remaining 30%. The fourth stage involved generating a landslide susceptibility map, classifying the area into zones of varying risk. Finally, we validated the model using a suite of performance metrics: accuracy, Root Mean Square Error (RMSE), Kappa coefficient, Mean Absolute Error (MAE), R-squared value, sensitivity, specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV). These metrics ensured a robust evaluation of the model’s predictive accuracy and reliability, in line with best practices in geohazard modeling.Results and DiscussionThe Artificial Neural Network (ANN) model demonstrated strong predictive performance, achieving an accuracy of 0.8543, RMSE of 0.123, Kappa coefficient of 0.79, MAE of 0.098, and R-squared value of 0.9916. These metrics confirm the model’s reliability in mapping landslide susceptibility across the Shahid Abbaspour Dam watershed. Analysis revealed three primary factors driving landslide risk in the area. Proximity to rivers emerged as the most influential, likely due to mechanisms such as bank erosion, increased soil moisture, and fluctuating groundwater levels. This finding aligns with Zakerinejad and Amoshahi (2022), who noted heightened risk within 100 meters of watercourses in similar regions. Slope gradient ranked as the second key factor, with steep slopes, particularly in the southwestern Dezful region, amplifying instability. Geological formations, especially the erosion-prone Gachsaran Formation and Quaternary alluvial deposits rich in marl, ranked third, underscoring their role in susceptibility. This observation echoes Mohammadi et al. (2022), who studied the Izeh and Deh Sheikh (Abbaspour Dam) basins. In contrast, elevation and slope curvature showed limited influence, differing from Selamat et al. (2022), where elevation was a primary factor, possibly due to the unique topographic and geologic traits of our study area. Other factors, such as NDVI and seismic activity, had moderate to minor roles, likely reflecting stable vegetation cover and low seismicity. The susceptibility map classified 31.3% of the area (75.37 km²) as high-risk zones, concentrated in the southwest and a small northern section. Sensitivity analysis across susceptibility classes showed consistent performance, with values ranging from 0.8060 for the lowest class to 0.8717 for the high-susceptibility class. These results highlight the ANN’s effectiveness and its alignment with regional patterns, offering a reliable tool for understanding landslide dynamics in the watershed.Conclusion This study successfully developed a landslide susceptibility map for the Shahid Abbaspour Dam watershed using an Artificial Neural Network, identifying key factors and high-risk zones. Proximity to rivers, slope gradient, and geological formations, particularly the Gachsaran Formation and Quaternary alluvium, emerged as the primary drivers of landslide risk. High-susceptibility zones, covering 31.3% of the area, were predominantly located in the southwestern and northern parts, providing critical insights for targeted interventions. The ANN model performed robustly, with an accuracy of 0.8543, RMSE of 0.123, Kappa of 0.79, MAE of 0.098, and R-squared of 0.9916, validating its predictive power. These metrics underscore the model’s reliability and its potential for application in similar watersheds prone to landslides. By quantifying the influence of environmental and terrain factors, this research offers a scientific foundation for land-use planning, infrastructure design, and disaster risk management in northeastern Khuzestan. The findings can guide policymakers and planners in prioritizing mitigation efforts, such as stabilizing slopes near rivers, regulating development on steep terrain, and monitoring vulnerable geological units. Future studies could refine this model by incorporating real-time data, such as rainfall forecasts or seismic monitoring, to enhance predictive accuracy and adaptability.Landslides are natural hazards that pose significant risks to human lives and the environment. Landslide susceptibility maps are vital tools for planning, management, and risk-reduction strategies. Various methods exist for generating these maps, but their effectiveness varies across studies. This research aimed to develop a landslide susceptibility map for the Shahid Abbaspour Dam watershed using an Artificial Neural Network (ANN). Landslide data points were randomly split into a 70% training and 30% testing dataset. Fifteen influencing factors were selected as model inputs, including elevation, slope, aspect, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), distance to roads and rivers, geology, soil texture, land use, and precipitation. Model validation was performed using accuracy metrics and statistical indices (RMSE, Cohen’s Kappa, MAE, and R²). The ANN model demonstrated strong predictive performance, achieving an accuracy of 0.8543. The study identified rivers as the most critical factor influencing landslide susceptibility in the area, followed by slope and geological formations. The highest susceptibility zones were found in the southwest and in a small northern section of the watershed. The resulting map provides valuable support for land-use planning and sustainable development strategies aimed at mitigating landslide risks in the region.Introduction Landslides rank among the most destructive natural hazards, endangering human lives, infrastructure, and the natural environment across the globe. In Iran, the northeastern part of Khuzestan Province, including the Shahid Abbaspour Dam watershed, is especially prone to landslides due to its mountainous topography, diverse geological formations, and seasonal rainfall patterns. Landslide susceptibility maps, which estimate the likelihood of occurrence based on local terrain and environmental conditions, are critical tools for planners, engineers, and policymakers. These maps guide land-use decisions, infrastructure development, and disaster preparedness, thereby reducing potential impacts on communities and ecosystems. Various methods, ranging from statistical models to machine learning techniques, have been employed to create such maps, each with varying degrees of accuracy and applicability. However, few studies have applied advanced machine learning approaches, such as Artificial Neural Networks (ANNs), in the Shahid Abbaspour Dam watershed, a critical area within the Greater Karun watershed. This research fills this gap by modeling landslide susceptibility in the region using an ANN, a method known for its ability to handle complex, non-linear relationships among multiple variables. The objective of this study is to produce a reliable susceptibility map to support risk mitigation, land-use planning, and sustainable development in this environmentally sensitive area. By leveraging the ANN’s predictive power, this study offers a robust, scientifically grounded approach to addressing a pressing regional challenge, contributing to the broader field of geohazard assessment.Material and MethodsThe study focuses on the Shahid Abbaspour Dam watershed, located in northeastern Khuzestan. This area, part of the Greater Karun watershed, features rugged terrain, diverse geology, and a history of landslide activity, making it ideal for susceptibility analysis. Our methodology was conducted in five comprehensive stages to assess the probability of landslides based on local conditions. First, we conducted an extensive literature review to identify relevant factors and gathered data from multiple sources. Second, we selected and prepared 15 influencing factors as spatial layers: elevation, slope gradient, slope aspect, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), distance from roads, distance from rivers, proximity to faults, geological formations, soil texture, land-use patterns, precipitation, Normalized Difference Vegetation Index (NDVI), and seismic activity. These datasets were derived from 1:50,000-scale topographic maps, 1:100,000-scale geological maps, and a 30-meter resolution Digital Elevation Model (DEM) from ASTER satellite imagery. In the third stage, we implemented an Artificial Neural Network (ANN) in the R programming environment using the nnet package. This machine learning approach excels at capturing non-linear patterns among variables, making it suitable for landslide prediction. The ANN was configured with a single hidden layer of 10 neurons, trained with 70% of the dataset, and tested with the remaining 30%. The fourth stage involved generating a landslide susceptibility map, classifying the area into zones of varying risk. Finally, we validated the model using a suite of performance metrics: accuracy, Root Mean Square Error (RMSE), Kappa coefficient, Mean Absolute Error (MAE), R-squared value, sensitivity, specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV). These metrics ensured a robust evaluation of the model’s predictive accuracy and reliability, in line with best practices in geohazard modeling.Results and DiscussionThe Artificial Neural Network (ANN) model demonstrated strong predictive performance, achieving an accuracy of 0.8543, RMSE of 0.123, Kappa coefficient of 0.79, MAE of 0.098, and R-squared value of 0.9916. These metrics confirm the model’s reliability in mapping landslide susceptibility across the Shahid Abbaspour Dam watershed. Analysis revealed three primary factors driving landslide risk in the area. Proximity to rivers emerged as the most influential, likely due to mechanisms such as bank erosion, increased soil moisture, and fluctuating groundwater levels. This finding aligns with Zakerinejad and Amoshahi (2022), who noted heightened risk within 100 meters of watercourses in similar regions. Slope gradient ranked as the second key factor, with steep slopes, particularly in the southwestern Dezful region, amplifying instability. Geological formations, especially the erosion-prone Gachsaran Formation and Quaternary alluvial deposits rich in marl, ranked third, underscoring their role in susceptibility. This observation echoes Mohammadi et al. (2022), who studied the Izeh and Deh Sheikh (Abbaspour Dam) basins. In contrast, elevation and slope curvature showed limited influence, differing from Selamat et al. (2022), where elevation was a primary factor, possibly due to the unique topographic and geologic traits of our study area. Other factors, such as NDVI and seismic activity, had moderate to minor roles, likely reflecting stable vegetation cover and low seismicity. The susceptibility map classified 31.3% of the area (75.37 km²) as high-risk zones, concentrated in the southwest and a small northern section. Sensitivity analysis across susceptibility classes showed consistent performance, with values ranging from 0.8060 for the lowest class to 0.8717 for the high-susceptibility class. These results highlight the ANN’s effectiveness and its alignment with regional patterns, offering a reliable tool for understanding landslide dynamics in the watershed.Conclusion This study successfully developed a landslide susceptibility map for the Shahid Abbaspour Dam watershed using an Artificial Neural Network, identifying key factors and high-risk zones. Proximity to rivers, slope gradient, and geological formations, particularly the Gachsaran Formation and Quaternary alluvium, emerged as the primary drivers of landslide risk. High-susceptibility zones, covering 31.3% of the area, were predominantly located in the southwestern and northern parts, providing critical insights for targeted interventions. The ANN model performed robustly, with an accuracy of 0.8543, RMSE of 0.123, Kappa of 0.79, MAE of 0.098, and R-squared of 0.9916, validating its predictive power. These metrics underscore the model’s reliability and its potential for application in similar watersheds prone to landslides. By quantifying the influence of environmental and terrain factors, this research offers a scientific foundation for land-use planning, infrastructure design, and disaster risk management in northeastern Khuzestan. The findings can guide policymakers and planners in prioritizing mitigation efforts, such as stabilizing slopes near rivers, regulating development on steep terrain, and monitoring vulnerable geological units. Future studies could refine this model by incorporating real-time data, such as rainfall forecasts or seismic monitoring, to enhance predictive accuracy and adaptability.https://geoeh.um.ac.ir/article_46895_6af924ebe7eec2218f8d11d3dd582d04.pdfFerdowsi University of MashhadJournal of Geography and Environmental Hazards2322-168214320250923Morphotectonic and Seismicity Investigation in the Northern and Southeastern of Central IranMorphotectonic and Seismicity Investigation in the Northern and Southeastern of Central Iran55714291810.22067/geoeh.2022.78334.1271FANajimeEskandari Nesab SiahkohiM.Sc. Student, Department of Geology and Earthquake Research Department, Shahid Bahonar University of Kerman, Kerman, IranMajidNematiProfessor, Department of Geology and Earthquake Research Group, Shahid Bahonar University of Kerman, Kerman, Iran0000-0001-6982-4110ShahramShafiei BaftiDepartment of Geology. Faculty of Sciences. Shahid Bahonar University of Kerman, Kerman, Iran0000-0002-4510-5493Journal Article20220821In the present study, the tectonics and seismicity of a part of central Iran were investigated. The study area includes the Lakarkuh, Ravar, Kuhbanan, Nayband, Tabas, Posht Badam, Cheshme Rostam, Bardsir, and Rafsanjan faults. In this research, the tectonic setting and active faults of the study area were first examined. The history of seismicity in this region, based on data from the Institute of Geophysics, University of Tehran, was reviewed, along with records of contemporary earthquakes from national seismic databases. These data show clear evidence of activity along the Lakarkuh, Kuhbanan, Ravar, Tabas, and Nayband faults. The presence of fault-displaced layers in the northern part of the study area, as observed in Google Earth imagery, confirms ongoing tectonic activity. Small earthquakes mostly occurred around the Tabas, Nayband, Lakarkuh, Bardsir, Davaran, Kuhbanan, and Rafsanjan faults, while larger earthquakes were concentrated at the Shahdad–Golbaf–Lakarkuh–Kuhbanan–Nayband multiple-fault junction. Analysis of earthquake mechanisms, using the first P-motion method and waveform modeling in Seisan software, revealed that some segments of the Kuhbanan fault exhibit reverse and right-lateral strike-slip movements. Based on potential future earthquakes, cities such as Tabas, Kuhbanan, Zarand, and Kerman, as well as many villages, are highly vulnerable to human and financial losses due to severe earthquakes. After compiling all seismic data, it was concluded that the surveyed area is at risk of devastating, high-intensity earthquakes. Therefore, seismological investigations in the eastern part of central Iran and surface geomorphological studies in the northern part of central Iran are crucial for understanding both the seismic and aseismic movements of faults in the region.IntroductionThe Iranian plateau, characterized by active faults, folding, volcanism, mountainous terrains, and variable crustal thickness, has historically been the site of numerous destructive earthquakes, caused by the convergence between the Eurasian and Arabian plates. In recent centuries, large-magnitude seismic events have occurred regularly, with seismicity concentrated in Central and Eastern Iran (removed "the"), particularly along orogenic belts such as the Dasht-e-Bayaz and Golbaf seismic zones. Notable events, including the 1978 Tabas, 2005 Zarand, and 2017 Hojedk earthquakes, underscore the importance of earthquake focal mechanism analysis in understanding crustal deformation patterns. Previous investigations, both global and regional, have examined active structures such as the Nayband, Kuhbanan, and Lakarkuh faults (corrected from Lekarkuh), integrating seismotectonic, geomorphic, and geological approaches. This study focuses on the Kuhbanan Fault and adjacent structures, assessing focal mechanisms of previously underexplored earthquakes and identifying young tectonic movements within the eastern and western sectors of Central Iran. While the eastern sector is seismically active, the western sector exhibits low seismicity, where geomorphological indicators become particularly valuable for detecting neotectonic activity.Study AreaThe investigated region forms part of Central Iran, encompassing the provinces of Kerman, Yazd, and portions of South Khorasan. This area contains numerous active faults, among which the Kuhbanan, Lakarkuh, Nayband, Golbaf, and Tabas faults are the most prominent. Geographically, the study area is located between longitudes 53°33′E and 57°53′E, and latitudes 29°59′N and 33°86′N (replaced "to" with "and" for precision).Materials and MethodsSeismic hazard assessment was initiated by compiling a comprehensive earthquake catalog, incorporating historical events (pre-1900) from Ambraseys and Melville (2005) and instrumental records (post-1900) from the Institute of Geophysics, University of Tehran. Focal mechanisms were determined for nine earthquakes (2006–present) using P-wave first-motion polarity analysis, applying the double-couple model with P- and T-axes, following the approach of Stein and Wysession (2009). While seismotectonic analysis was prioritized in the active eastern sector of Central Iran, geomorphic and structural mapping using Google Earth imagery was employed to investigate neotectonic features in the low-seismicity western sector.Discussion and ResultsApproximately four-fifths of Iran is occupied by the folded mountain belts of the Zagros and Alborz ranges, while Central Iran comprises low-lying areas with complex tectonic structures. In the low-seismicity western sector of Central Iran, geomorphic evidence such as offsets of young strata along faults was used to infer neotectonic activity. Examples include a 3.19 km right-lateral displacement southwest of Tabas, likely associated with the northern branches of the Kuhbanan Fault, and a 1.09 km right-lateral offset near the Posht-Badam Fault.Since 2006, a total of 6,496 earthquakes have been recorded in the study area, the majority with magnitudes below 4.0 and concentrated mainly in the eastern sector near the Kuhbanan and Lakarkuh faults. Major events include the 1923 Bardsir (M 5.5), 1981 Golbaf (M 7.1), and 2017 Hojedk (M 6.1) earthquakes. Focal mechanism analysis of nine well-constrained earthquakes (2006–2011) revealed a clustering of seismicity in the eastern part of the study area, highlighting the role of the Kuhbanan and Lakarkuh faults in accommodating active tectonic deformation. ConclusionsAnalysis of young stratal displacements in the northern and western parts of Central Iran indicates an active tectonic regime, with major structures such as the Kuhbanan and Lekarkuh faults playing a dominant role. Although the region’s overall seismicity is lower than that of other tectonic provinces in Iran, most earthquakes are concentrated in its eastern sector near these principal faults. Among recorded events, nine moderate-magnitude earthquakes had sufficient data for focal mechanism analysis, predominantly showing reverse and right-lateral strike-slip motion consistent with the orientation of the Kuhbanan and Lekarkuh faults. The findings suggest a significant potential for future large and destructive earthquakes, particularly if larger fault segments become reactivated.In the present study, the tectonics and seismicity of a part of central Iran were investigated. The study area includes the Lakarkuh, Ravar, Kuhbanan, Nayband, Tabas, Posht Badam, Cheshme Rostam, Bardsir, and Rafsanjan faults. In this research, the tectonic setting and active faults of the study area were first examined. The history of seismicity in this region, based on data from the Institute of Geophysics, University of Tehran, was reviewed, along with records of contemporary earthquakes from national seismic databases. These data show clear evidence of activity along the Lakarkuh, Kuhbanan, Ravar, Tabas, and Nayband faults. The presence of fault-displaced layers in the northern part of the study area, as observed in Google Earth imagery, confirms ongoing tectonic activity. Small earthquakes mostly occurred around the Tabas, Nayband, Lakarkuh, Bardsir, Davaran, Kuhbanan, and Rafsanjan faults, while larger earthquakes were concentrated at the Shahdad–Golbaf–Lakarkuh–Kuhbanan–Nayband multiple-fault junction. Analysis of earthquake mechanisms, using the first P-motion method and waveform modeling in Seisan software, revealed that some segments of the Kuhbanan fault exhibit reverse and right-lateral strike-slip movements. Based on potential future earthquakes, cities such as Tabas, Kuhbanan, Zarand, and Kerman, as well as many villages, are highly vulnerable to human and financial losses due to severe earthquakes. After compiling all seismic data, it was concluded that the surveyed area is at risk of devastating, high-intensity earthquakes. Therefore, seismological investigations in the eastern part of central Iran and surface geomorphological studies in the northern part of central Iran are crucial for understanding both the seismic and aseismic movements of faults in the region.IntroductionThe Iranian plateau, characterized by active faults, folding, volcanism, mountainous terrains, and variable crustal thickness, has historically been the site of numerous destructive earthquakes, caused by the convergence between the Eurasian and Arabian plates. In recent centuries, large-magnitude seismic events have occurred regularly, with seismicity concentrated in Central and Eastern Iran (removed "the"), particularly along orogenic belts such as the Dasht-e-Bayaz and Golbaf seismic zones. Notable events, including the 1978 Tabas, 2005 Zarand, and 2017 Hojedk earthquakes, underscore the importance of earthquake focal mechanism analysis in understanding crustal deformation patterns. Previous investigations, both global and regional, have examined active structures such as the Nayband, Kuhbanan, and Lakarkuh faults (corrected from Lekarkuh), integrating seismotectonic, geomorphic, and geological approaches. This study focuses on the Kuhbanan Fault and adjacent structures, assessing focal mechanisms of previously underexplored earthquakes and identifying young tectonic movements within the eastern and western sectors of Central Iran. While the eastern sector is seismically active, the western sector exhibits low seismicity, where geomorphological indicators become particularly valuable for detecting neotectonic activity.Study AreaThe investigated region forms part of Central Iran, encompassing the provinces of Kerman, Yazd, and portions of South Khorasan. This area contains numerous active faults, among which the Kuhbanan, Lakarkuh, Nayband, Golbaf, and Tabas faults are the most prominent. Geographically, the study area is located between longitudes 53°33′E and 57°53′E, and latitudes 29°59′N and 33°86′N (replaced "to" with "and" for precision).Materials and MethodsSeismic hazard assessment was initiated by compiling a comprehensive earthquake catalog, incorporating historical events (pre-1900) from Ambraseys and Melville (2005) and instrumental records (post-1900) from the Institute of Geophysics, University of Tehran. Focal mechanisms were determined for nine earthquakes (2006–present) using P-wave first-motion polarity analysis, applying the double-couple model with P- and T-axes, following the approach of Stein and Wysession (2009). While seismotectonic analysis was prioritized in the active eastern sector of Central Iran, geomorphic and structural mapping using Google Earth imagery was employed to investigate neotectonic features in the low-seismicity western sector.Discussion and ResultsApproximately four-fifths of Iran is occupied by the folded mountain belts of the Zagros and Alborz ranges, while Central Iran comprises low-lying areas with complex tectonic structures. In the low-seismicity western sector of Central Iran, geomorphic evidence such as offsets of young strata along faults was used to infer neotectonic activity. Examples include a 3.19 km right-lateral displacement southwest of Tabas, likely associated with the northern branches of the Kuhbanan Fault, and a 1.09 km right-lateral offset near the Posht-Badam Fault.Since 2006, a total of 6,496 earthquakes have been recorded in the study area, the majority with magnitudes below 4.0 and concentrated mainly in the eastern sector near the Kuhbanan and Lakarkuh faults. Major events include the 1923 Bardsir (M 5.5), 1981 Golbaf (M 7.1), and 2017 Hojedk (M 6.1) earthquakes. Focal mechanism analysis of nine well-constrained earthquakes (2006–2011) revealed a clustering of seismicity in the eastern part of the study area, highlighting the role of the Kuhbanan and Lakarkuh faults in accommodating active tectonic deformation. ConclusionsAnalysis of young stratal displacements in the northern and western parts of Central Iran indicates an active tectonic regime, with major structures such as the Kuhbanan and Lekarkuh faults playing a dominant role. Although the region’s overall seismicity is lower than that of other tectonic provinces in Iran, most earthquakes are concentrated in its eastern sector near these principal faults. Among recorded events, nine moderate-magnitude earthquakes had sufficient data for focal mechanism analysis, predominantly showing reverse and right-lateral strike-slip motion consistent with the orientation of the Kuhbanan and Lekarkuh faults. The findings suggest a significant potential for future large and destructive earthquakes, particularly if larger fault segments become reactivated.https://geoeh.um.ac.ir/article_42918_a7e8c3f6e5ea8d4fd52278b05d7747b3.pdfFerdowsi University of MashhadJournal of Geography and Environmental Hazards2322-168214320250923Assessing the Effects of Land Use Change on Flood Occurrence in the Mordaq Chai Watershed Using the SWAT and Artificial Neural Network ModelsAssessing the Effects of Land Use Change on Flood Occurrence in the Mordaq Chai Watershed Using the SWAT and Artificial Neural Network Models72924689710.22067/geoeh.2025.92648.1558FAMohammad HosseinRezaei MoghaddamProfessor in Geomorphology, Faculty of Planning and Environmental Sciences, University of Tabriz and Iranian Hazardology Association, Tabriz, Iran0000-0003-2626-4656AtefehHaghighatM. Sc in Geomorphology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran0009-0008-9024-0708TohidRahimpourPostdoctoral Researcher in Geomorphology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran0000-0002-4034-8971AbdollahBehboodyPh.D in Geomorphology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, IranJournal Article20250314Over the past few decades, significant changes have occurred in the Mardagh Chay watershed, primarily due to the development of agricultural lands, the expansion of settlements, and other activities. During this period, several events such as surface flows and river overflows have taken place, leading to flooding in settlements and damage to infrastructure. Accordingly, the present study examines and evaluates the impact of land use changes on flooding in the Mardagh Chay watershed in East Azerbaijan Province. First, land use classification was performed using an artificial neural network model, and land use maps were generated for 2001, 2011, and 2021. Subsequently, the SWAT model was applied to the classification results. According to the findings, over a period of twenty years, with the exception of barren lands, all other land use classes showed a dominant increasing trend, with the rangelands of the region experiencing the greatest expansion. The results of the SWAT model also revealed that the runoff trend in the Mardagh Chay basin has largely corresponded with land use changes during this period. In particular, the growth of pasture and built-up areas in the region has had a direct impact on the increase in runoff in the southern areas. Considering the effect of the topography and slope of the basin on flow trends, as well as the greater imperviousness of land surfaces in the southern areas compared to others, the flood potential in these areas has been determined to be significantly higher.Introduction Floods are considered one of the most important and abundant geomorphic hazards in the country, which cause a lot of damage every year. Recent events in the course of the Mordaq River have indicated the frequency and intensity of floods in the basin. However, the changes in hydrological and climatic regimes of the basin and what causes these changes in the basin have not been well studied. During the last few decades, significant developments have occurred in the Mordaq chai basin with the aim of developing agricultural lands, expanding settlements and other purposes. During this period, several events have been observed in surface flows and river flooding, including the flooding of settlements and the destruction of infrastructure during the rainy season. The present research has evaluated the impact of land use changes on floods in Mordaq chai basin in East Azerbaijan province.Material and MethodsMordaq Chai basin is located at latitudes between 37° 16΄ and 37° 44΄ north, and at longitudes between 46° 21΄ and 46° 30΄ east. The basin area is about 332 Km2. The elevation variations of the basin range from 1567 meters at the outlet of the basin to 3693 meters in Sahand Mountain. In this study land use classification was done using artificial neural network model and land use map was obtained in 2001, 2011 and 2021. Then, the classification results were compared, for which the LCM model was used. The SWAT model has been implemented on the results of land use classification. In addition, the sensitivity assessment of the results has been considered in two phases of calibration and validation, in this regard, land use in 2001 was recalibrated in the statistical period of 1985-1999 and validated in the statistical period of 2000-2001. Also, the land use of 2011 was calibrated in the statistical period of 2002-2009 and validated in the statistical period of 2010-2011 and the land use of 2021 was calibrated in the statistical period of 2012-2019 and validated in the statistical period of 2020-2021.Results and DiscussionOver the course of twenty years, with the exception of barren lands, other land use classes have shown a predominant upward trend, and the pastures in the region have witnessed the most significant expansion in terms of area. This shift highlights notable changes in land use patterns across the area. The increase in pastureland could be attributed to various factors, such as changes in agricultural practices, conservation policies, or even natural shifts in the ecosystem. This growth in pastures may have positive implications for biodiversity conservation, soil erosion prevention, and overall environmental improvement in the region. However, to fully understand the underlying reasons for these changes and their long-term impacts, a more detailed analysis and further data examination are required. According to the physiography and topography governing the basin, the situation is such that in the northern half of the region, we see more natural land cover (including snow, water, barren and pasture) than in the southern half, where human activities (gardens and built-up lands) dominate. The role of Mordaq Chai river in this field has also been significant and for example, most of the gardens of the region are distributed linearly next to this river and its branches, or the villages of the region that have the largest area of built-up land are located in the vicinity of these rivers. In 2001, wasteland, pastures, and gardens accounted for the largest share of existing land uses at 61.21%, 29.1%, and 8.01%, respectively, and snow, built-up areas, and water were next in rank at 1.59%, 0.073%, and 0.016%, respectively. In 2021, the relative share of barren lands has decreased sharply and reached 28.21% of the entire region, and on the other hand, pastures have grown a lot and their relative share in the region has become 54.45%.ConclusionsThe simulation outcomes generated by the SWAT model revealed certain limitations in its application within the Mordaq Chai basin. Specifically, the calibration and validation phases at the overarching basin scale, which incorporated land use data from varying years, yielded suboptimal and unsatisfactory results. This primary deficiency was quantitatively demonstrated by the fact that the observed runoff data points frequently fell outside the simulated uncertainty range predicted by the model. The root cause of this discrepancy has been identified as a fundamental inadequacy in the model's algorithmic representation of snowmelt-driven runoff. The SWAT model appears to systematically miscalculate the volume and timing of runoff originating from the melting snowpack, particularly during the critical hydrological period spanning from late winter through late spring. This improper estimation significantly compromises the accuracy of the overall surface runoff simulation, as snowmelt is a major contributor to the basin's hydrologic regime. Furthermore, the analysis of long-term trends established a strong correlation between the hydrological behavior of the basin and anthropogenic changes to the landscape. The recorded trajectory of runoff in the Mordaq Chai basin has been largely congruent with and influenced by the concurrent pattern of land use and land cover change (LULC) over the same period. A direct causal relationship was observed: the expansion of two specific land use classes—pastureland and built-up/urbanized areas—within the southern regions of the watershed has acted as a primary driver for increased runoff generation. This phenomenon is exacerbated by the underlying physiographic characteristics of the basin. The southern areas are distinguished by a more pronounced topography and steeper slopes, which naturally accelerate flow velocity and reduce concentration times. Coupled with this, the land surface in these southern zones has become increasingly impervious due to urbanization and the degradation of natural vegetation cover. This combination of factors—steeper slopes and reduced infiltration capacity—creates a synergistic effect that drastically elevates the flood potential, making these southern areas disproportionately more vulnerable to high-flow events and flooding compared to other parts of the basin.Acknowledgements We are grateful to all the scientific consultants of this paper.Over the past few decades, significant changes have occurred in the Mardagh Chay watershed, primarily due to the development of agricultural lands, the expansion of settlements, and other activities. During this period, several events such as surface flows and river overflows have taken place, leading to flooding in settlements and damage to infrastructure. Accordingly, the present study examines and evaluates the impact of land use changes on flooding in the Mardagh Chay watershed in East Azerbaijan Province. First, land use classification was performed using an artificial neural network model, and land use maps were generated for 2001, 2011, and 2021. Subsequently, the SWAT model was applied to the classification results. According to the findings, over a period of twenty years, with the exception of barren lands, all other land use classes showed a dominant increasing trend, with the rangelands of the region experiencing the greatest expansion. The results of the SWAT model also revealed that the runoff trend in the Mardagh Chay basin has largely corresponded with land use changes during this period. In particular, the growth of pasture and built-up areas in the region has had a direct impact on the increase in runoff in the southern areas. Considering the effect of the topography and slope of the basin on flow trends, as well as the greater imperviousness of land surfaces in the southern areas compared to others, the flood potential in these areas has been determined to be significantly higher.Introduction Floods are considered one of the most important and abundant geomorphic hazards in the country, which cause a lot of damage every year. Recent events in the course of the Mordaq River have indicated the frequency and intensity of floods in the basin. However, the changes in hydrological and climatic regimes of the basin and what causes these changes in the basin have not been well studied. During the last few decades, significant developments have occurred in the Mordaq chai basin with the aim of developing agricultural lands, expanding settlements and other purposes. During this period, several events have been observed in surface flows and river flooding, including the flooding of settlements and the destruction of infrastructure during the rainy season. The present research has evaluated the impact of land use changes on floods in Mordaq chai basin in East Azerbaijan province.Material and MethodsMordaq Chai basin is located at latitudes between 37° 16΄ and 37° 44΄ north, and at longitudes between 46° 21΄ and 46° 30΄ east. The basin area is about 332 Km2. The elevation variations of the basin range from 1567 meters at the outlet of the basin to 3693 meters in Sahand Mountain. In this study land use classification was done using artificial neural network model and land use map was obtained in 2001, 2011 and 2021. Then, the classification results were compared, for which the LCM model was used. The SWAT model has been implemented on the results of land use classification. In addition, the sensitivity assessment of the results has been considered in two phases of calibration and validation, in this regard, land use in 2001 was recalibrated in the statistical period of 1985-1999 and validated in the statistical period of 2000-2001. Also, the land use of 2011 was calibrated in the statistical period of 2002-2009 and validated in the statistical period of 2010-2011 and the land use of 2021 was calibrated in the statistical period of 2012-2019 and validated in the statistical period of 2020-2021.Results and DiscussionOver the course of twenty years, with the exception of barren lands, other land use classes have shown a predominant upward trend, and the pastures in the region have witnessed the most significant expansion in terms of area. This shift highlights notable changes in land use patterns across the area. The increase in pastureland could be attributed to various factors, such as changes in agricultural practices, conservation policies, or even natural shifts in the ecosystem. This growth in pastures may have positive implications for biodiversity conservation, soil erosion prevention, and overall environmental improvement in the region. However, to fully understand the underlying reasons for these changes and their long-term impacts, a more detailed analysis and further data examination are required. According to the physiography and topography governing the basin, the situation is such that in the northern half of the region, we see more natural land cover (including snow, water, barren and pasture) than in the southern half, where human activities (gardens and built-up lands) dominate. The role of Mordaq Chai river in this field has also been significant and for example, most of the gardens of the region are distributed linearly next to this river and its branches, or the villages of the region that have the largest area of built-up land are located in the vicinity of these rivers. In 2001, wasteland, pastures, and gardens accounted for the largest share of existing land uses at 61.21%, 29.1%, and 8.01%, respectively, and snow, built-up areas, and water were next in rank at 1.59%, 0.073%, and 0.016%, respectively. In 2021, the relative share of barren lands has decreased sharply and reached 28.21% of the entire region, and on the other hand, pastures have grown a lot and their relative share in the region has become 54.45%.ConclusionsThe simulation outcomes generated by the SWAT model revealed certain limitations in its application within the Mordaq Chai basin. Specifically, the calibration and validation phases at the overarching basin scale, which incorporated land use data from varying years, yielded suboptimal and unsatisfactory results. This primary deficiency was quantitatively demonstrated by the fact that the observed runoff data points frequently fell outside the simulated uncertainty range predicted by the model. The root cause of this discrepancy has been identified as a fundamental inadequacy in the model's algorithmic representation of snowmelt-driven runoff. The SWAT model appears to systematically miscalculate the volume and timing of runoff originating from the melting snowpack, particularly during the critical hydrological period spanning from late winter through late spring. This improper estimation significantly compromises the accuracy of the overall surface runoff simulation, as snowmelt is a major contributor to the basin's hydrologic regime. Furthermore, the analysis of long-term trends established a strong correlation between the hydrological behavior of the basin and anthropogenic changes to the landscape. The recorded trajectory of runoff in the Mordaq Chai basin has been largely congruent with and influenced by the concurrent pattern of land use and land cover change (LULC) over the same period. A direct causal relationship was observed: the expansion of two specific land use classes—pastureland and built-up/urbanized areas—within the southern regions of the watershed has acted as a primary driver for increased runoff generation. This phenomenon is exacerbated by the underlying physiographic characteristics of the basin. The southern areas are distinguished by a more pronounced topography and steeper slopes, which naturally accelerate flow velocity and reduce concentration times. Coupled with this, the land surface in these southern zones has become increasingly impervious due to urbanization and the degradation of natural vegetation cover. This combination of factors—steeper slopes and reduced infiltration capacity—creates a synergistic effect that drastically elevates the flood potential, making these southern areas disproportionately more vulnerable to high-flow events and flooding compared to other parts of the basin.Acknowledgements We are grateful to all the scientific consultants of this paper.https://geoeh.um.ac.ir/article_46897_e34e51889b063b3158e587de76dd4129.pdfFerdowsi University of MashhadJournal of Geography and Environmental Hazards2322-168214320250923Structural Analysis and Regime Shift of Drought in Iran Using a Three-Dimensional Clustering ApproachStructural Analysis and Regime Shift of Drought in Iran Using a Three-Dimensional Clustering Approach931154709210.22067/geoeh.2025.94525.1597FAJavadMahdizadeh SoulaDepartment of Physical Geography, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran0000-0001-5211-4286YousefGhavidel RahimiDepartment of Physical Geography, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran0000-0003-1929-155XManuchehrFarajzadehDepartment of Physical Geography, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran0000-0002-4916-9304Journal Article20250605This study investigates the structural evolution and regime shifts of droughts in Iran from 1958 to 2022 using a three-dimensional (3D) clustering framework. The analysis employed high-resolution (~4 km) climate data from the TerraClimate dataset. Drought clusters were identified as continuous spatiotemporal events by applying thresholding to the Self-Calibrating Palmer Drought Severity Index (scPDSI), a widely used measure of drought intensity, and extracting connected components within the data cube. To refine the results, a minimum-size filter was applied, with the optimal value determined through a geometric optimization method based on curvature analysis (cos C method). The findings reveal a substantial transformation in Iran’s drought patterns, particularly after 2000, when short, sporadic episodes gave way to more persistent and spatially extensive drought systems. Among the five drought thresholds tested, the moderate level (scPDSI = -2.0) provided the most balanced trade-off between event frequency and spatial coverage. In parallel, a minimum cluster size of 16 voxels was identified as the geometric filter threshold, effectively reducing noise from small, transient clusters while preserving significant drought events and enhancing structural coherence. Results show that, since 2000, drought clusters have become larger, longer-lasting, and more spatially synchronized. Moreover, regions in central, western, northeastern, and northwestern Iran-previously less affected by severe drought-have emerged as new hotspots, experiencing marked increases in both frequency and duration. By integrating 3D clustering with threshold optimization, this study introduces a methodological innovation in drought research. The approach not only advances scientific understanding of spatiotemporal drought dynamics but also provides practical value for improving drought monitoring, informing adaptation planning, and strengthening early warning systems in Iran and other arid regions.Introduction Drought is a complex environmental hazard with profound ecological, agricultural, and social impacts. In Iran’s arid and semi-arid climates, its increasing frequency and severity have heightened risks to water and land resources. Conventional studies often rely on indices such as the Standardized Precipitation Index (SPI) and the Palmer Drought Severity Index (PDSI), but these approaches typically lack the spatiotemporal continuity needed to capture drought dynamics fully.To address this limitation, we applied a three-dimensional (3D) clustering framework to monthly Self-Calibrating PDSI (scPDSI) data (1958–2022) from the high-resolution TerraClimate dataset. Using connected-component labeling and geometric threshold optimization, we identified and analyzed coherent spatiotemporal drought clusters across Iran. The objective was to evaluate structural patterns and regime shifts in drought behavior, offering a novel contribution to both drought monitoring and adaptation planning.Material and Methods We adopted a multi-step observational framework, adapted from Diaz et al. (2024), to analyze drought events in Iran (1958–2022). The process involved: (1) 3D connected-component labeling to identify continuous drought clusters; (2) cluster-size filtering to eliminate insignificant structures; and (3) threshold sensitivity analysis to determine optimal drought intensity levels.Drought severity was measured using scPDSI derived from TerraClimate (~4 km resolution gridded climate variables). Data were processed in MATLAB by constructing a 3D data cube (latitude–longitude–time) and classifying drought conditions with five thresholds (–0.5 to –4.0). The resulting binary maps enabled the detection, filtering, and analysis of major drought events.Results and Discussion Over 7,400 drought clusters were identified across Iran between 1958 and 2022. Cluster number, size, and duration varied strongly with both scPDSI threshold and minimum-size filter. At more lenient thresholds (e.g., –0.5), clusters were abundant but small and short-lived; at more stringent thresholds (e.g., –4), clusters were fewer but spatially extensive and temporally persistent.Geometric optimization (cos C method, based on curvature analysis) identified an optimal threshold of scPDSI = –2 with a minimum cluster size of 16 voxels. This balance retained hydrologically relevant events while reducing noise. Temporal analysis revealed a rising trend in the percentage of land under drought (PDA) since 2000, with increasingly frequent and spatially synchronized episodes. Moderate droughts (–2) peaked in the late 1960s, early 1980s, and after 2000, while extreme droughts (–4) intensified post-2000. Spatial mapping showed persistent droughts in central, western, and northeastern provinces, with newly emerging hotspots in Golestan, Ardabil, and Razavi Khorasan.These results suggest a regime shift in Iran’s drought behavior over the past two decades, characterized by events that are more chronic, widespread, and synchronized. Such findings highlight the inadequacy of short-term or localized interventions and emphasize the need for integrated, long-term adaptation strategies.ConclusionThis study presents a comprehensive structural analysis of drought events in Iran (1958–2022) using a novel 3D clustering framework applied to high-resolution scPDSI data. The approach captured droughts as continuous spatiotemporal entities, independent of administrative or station-based constraints.Findings reveal a post-2000 regime shift toward longer, more frequent, and more extensive droughts, likely linked to climate change and large-scale ocean–atmosphere variability. Optimal detection was achieved with scPDSI = –2.0 and a cluster-size filter of 16 voxels, which balanced event frequency and reliability. Spatial analysis further identified newly emerging drought hotspots in central, western, and northern provinces, underscoring the need for updated risk maps and adaptive strategies.The 3D clustering approach offers a transferable tool for advanced drought monitoring in arid regions globally. Future research should integrate climate change projections and link drought clusters to socioeconomic and ecological impacts to strengthen resilience at national and regional scales. This study investigates the structural evolution and regime shifts of droughts in Iran from 1958 to 2022 using a three-dimensional (3D) clustering framework. The analysis employed high-resolution (~4 km) climate data from the TerraClimate dataset. Drought clusters were identified as continuous spatiotemporal events by applying thresholding to the Self-Calibrating Palmer Drought Severity Index (scPDSI), a widely used measure of drought intensity, and extracting connected components within the data cube. To refine the results, a minimum-size filter was applied, with the optimal value determined through a geometric optimization method based on curvature analysis (cos C method). The findings reveal a substantial transformation in Iran’s drought patterns, particularly after 2000, when short, sporadic episodes gave way to more persistent and spatially extensive drought systems. Among the five drought thresholds tested, the moderate level (scPDSI = -2.0) provided the most balanced trade-off between event frequency and spatial coverage. In parallel, a minimum cluster size of 16 voxels was identified as the geometric filter threshold, effectively reducing noise from small, transient clusters while preserving significant drought events and enhancing structural coherence. Results show that, since 2000, drought clusters have become larger, longer-lasting, and more spatially synchronized. Moreover, regions in central, western, northeastern, and northwestern Iran-previously less affected by severe drought-have emerged as new hotspots, experiencing marked increases in both frequency and duration. By integrating 3D clustering with threshold optimization, this study introduces a methodological innovation in drought research. The approach not only advances scientific understanding of spatiotemporal drought dynamics but also provides practical value for improving drought monitoring, informing adaptation planning, and strengthening early warning systems in Iran and other arid regions.Introduction Drought is a complex environmental hazard with profound ecological, agricultural, and social impacts. In Iran’s arid and semi-arid climates, its increasing frequency and severity have heightened risks to water and land resources. Conventional studies often rely on indices such as the Standardized Precipitation Index (SPI) and the Palmer Drought Severity Index (PDSI), but these approaches typically lack the spatiotemporal continuity needed to capture drought dynamics fully.To address this limitation, we applied a three-dimensional (3D) clustering framework to monthly Self-Calibrating PDSI (scPDSI) data (1958–2022) from the high-resolution TerraClimate dataset. Using connected-component labeling and geometric threshold optimization, we identified and analyzed coherent spatiotemporal drought clusters across Iran. The objective was to evaluate structural patterns and regime shifts in drought behavior, offering a novel contribution to both drought monitoring and adaptation planning.Material and Methods We adopted a multi-step observational framework, adapted from Diaz et al. (2024), to analyze drought events in Iran (1958–2022). The process involved: (1) 3D connected-component labeling to identify continuous drought clusters; (2) cluster-size filtering to eliminate insignificant structures; and (3) threshold sensitivity analysis to determine optimal drought intensity levels.Drought severity was measured using scPDSI derived from TerraClimate (~4 km resolution gridded climate variables). Data were processed in MATLAB by constructing a 3D data cube (latitude–longitude–time) and classifying drought conditions with five thresholds (–0.5 to –4.0). The resulting binary maps enabled the detection, filtering, and analysis of major drought events.Results and Discussion Over 7,400 drought clusters were identified across Iran between 1958 and 2022. Cluster number, size, and duration varied strongly with both scPDSI threshold and minimum-size filter. At more lenient thresholds (e.g., –0.5), clusters were abundant but small and short-lived; at more stringent thresholds (e.g., –4), clusters were fewer but spatially extensive and temporally persistent.Geometric optimization (cos C method, based on curvature analysis) identified an optimal threshold of scPDSI = –2 with a minimum cluster size of 16 voxels. This balance retained hydrologically relevant events while reducing noise. Temporal analysis revealed a rising trend in the percentage of land under drought (PDA) since 2000, with increasingly frequent and spatially synchronized episodes. Moderate droughts (–2) peaked in the late 1960s, early 1980s, and after 2000, while extreme droughts (–4) intensified post-2000. Spatial mapping showed persistent droughts in central, western, and northeastern provinces, with newly emerging hotspots in Golestan, Ardabil, and Razavi Khorasan.These results suggest a regime shift in Iran’s drought behavior over the past two decades, characterized by events that are more chronic, widespread, and synchronized. Such findings highlight the inadequacy of short-term or localized interventions and emphasize the need for integrated, long-term adaptation strategies.ConclusionThis study presents a comprehensive structural analysis of drought events in Iran (1958–2022) using a novel 3D clustering framework applied to high-resolution scPDSI data. The approach captured droughts as continuous spatiotemporal entities, independent of administrative or station-based constraints.Findings reveal a post-2000 regime shift toward longer, more frequent, and more extensive droughts, likely linked to climate change and large-scale ocean–atmosphere variability. Optimal detection was achieved with scPDSI = –2.0 and a cluster-size filter of 16 voxels, which balanced event frequency and reliability. Spatial analysis further identified newly emerging drought hotspots in central, western, and northern provinces, underscoring the need for updated risk maps and adaptive strategies.The 3D clustering approach offers a transferable tool for advanced drought monitoring in arid regions globally. Future research should integrate climate change projections and link drought clusters to socioeconomic and ecological impacts to strengthen resilience at national and regional scales. https://geoeh.um.ac.ir/article_47092_a8b8f6be161a4bdcabcc947b3e72f8b2.pdfFerdowsi University of MashhadJournal of Geography and Environmental Hazards2322-168214320250923Forecasting and Trending of Precipitation in Selected Cities of the Northern Half of Iran Using ACCESS and CNRM ModelsForecasting and Trending of Precipitation in Selected Cities of the Northern Half of Iran Using ACCESS and CNRM Models1161394698010.22067/geoeh.2025.93054.1565FARoghayehMaleki MereshtPostdoctoral Researcher in Climatology, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran0000-0003-1803-4316BromandSalahiProfessor in Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran0000-0003-4826-6185MahnazSaberPostdoctoral Researcher in Climatology, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, IranJournal ArticleClimate change has led to alterations in the temporal and spatial distribution of precipitation across the globe. The present study aims to identify trends and project future precipitation in selected cities of northern Iran. To achieve this goal, the 30-year precipitation trend (1994–2023) for each city was first examined using the Mann-Kendall test and Sen’s slope estimator. In the second phase, precipitation was projected for the next 20 years (2040–2060) in the study area using the latest version of the LARS-WG8 weather generator under two scenarios—SSP2-4.5 and SSP5-8.5—with the ACCESS-ESM1-5 and CNRM-CM6-1 models. The outputs of both models were then evaluated and compared using validation indices (R², MAPE, and RMSE) to select the most suitable model. Trend analysis results indicated that over the past 30 years (1994–2023), the precipitation pattern in all stations in the region changed significantly, showing a decreasing trend at the 99% confidence level. This decrease was greater in the cities of Qazvin, Ardabil, and Zanjan, and less in Rasht and Gorgan. According to the projections from the two models, ACCESS-ESM1-5 and CNRM-CM6, over the next 20 years (2040–2060), the entire study area will face a decrease in precipitation in most months of the year. The decrease in precipitation in the northwest will be more than 60% in some months. Conversely, some cities will experience an increase in precipitation during the warm seasons, which will be more evident in low-precipitation cities such as Semnan. Finally, the comparison of the total annual precipitation of the observed and forecast periods confirmed a significant decrease in precipitation across the entire study area compared to the base period. The research findings indicate temporal and spatial anomalies in precipitation, confirm the effects of climate change on the region, and emphasize the need to adopt efficient management policies to optimize water resource consumption and develop strategies for adapting to climate change.Introduction Climate change has altered the temporal and spatial distribution of precipitation and its intensity throughout the world. Precipitation description, analysis, and modeling are among the most challenging issues in climate research, reflecting its complex and irregular temporal and spatial structure. In this regard, several modeling methods have been developed to deal with this complexity (Sánchez et al., 2011). A reduction in the number of rainy days and an increase in precipitation intensity over limited days can increase the probability of flooding in areas such as the coastal cities of the Caspian Sea, as well as cause soil erosion and reduce water infiltration into groundwater resources.Material and MethodsIn the present study, the trend and forecast of precipitation in selected cities in northern Iran (Ardabil, Tabriz, Bojnourd, Mashhad, Gorgan, Semnan, Zanjan, Tehran, Urmia, Gorgan, Rasht and, Sanandaj) were investigated. To analyzed the total annual precipitation, data from the last 30 years (1994-2023) were obtained from the Iranian Meteorological Organization, and Sen's slope estimator, a non-parametric method for estimating the slope of the regression line in trend analysis, was applied.Results and DiscussionAccording to the results of the trend analysis, during the statistical period (1994–2023), the Q statistic (Sen’s slope estimator) and Z (Mann-Kendall test) were negative for all stations, indicating that the total annual precipitation in all 12 cities showed a decreasing trend. The largest Q values were observed in Qazvin (-81.8), Ardabil (-63.8), and Zanjan (-45.8). The highest negative Z values occurred in Sanandaj (-4.62), Zanjan (-4.57), and Qazvin (-4.42), while the lowest was in Rasht (-2.98). Since the Z scores in all cities were less than -2.58, a significant downward trend in precipitation during the past 30 years is confirmed at the 99% confidence level.In the second part of the study, after validating the performance of LARS-WG8 in simulating base-period precipitation (1980–2010), the ACCESS-ESM1-5 and CNRM-CM6-1 models were used to predict future precipitation under the SSP2-4.5 and SSP5-8.5 scenarios. According to the results, Urmia is projected to experience decreased precipitation in all months under both models. Tehran, Tabriz, Ardabil, Gorgan, and Rasht will also face significant reductions in most months, with only slight increases (less than 20%) under some scenarios. Zanjan, Sanandaj, Qazvin, Mashhad, Bojnourd, and Semnan are expected to experience up to a 50% increase in precipitation in at least one late-spring or summer month. Overall, projections from both models suggest that all 12 cities will experience a significant reduction in precipitation compared to the long-term average during most months of the next 20 years.Finally, model accuracy was assessed using R², RMSE, and MAPE. Although both models showed relatively high error rates, each city exhibited higher accuracy under one model. Consequently, the CNRM model was selected for Sanandaj, Ardabil, Tehran, and Gorgan, while the ACCESS model was selected for the remaining cities.ConclusionThe aim of the present study was to forecast and analyze precipitation trends in selected cities of northern Iran using GCM models, in order to reveal the extent of climate change impacts on different climatic regions. Based on Sen’s slope estimator and Mann-Kendall tests, precipitation in all cities during the past 30 years (1994–2023) showed a significant decreasing trend at the 99% confidence level. The decrease was most pronounced in mountainous cities in the northwest (Qazvin, Ardabil, and Zanjan) and least in humid coastal cities (Rasht and Gorgan).Projections from the ACCESS and CNRM models suggest that precipitation patterns in the study area will undergo significant changes. Over the next 20 years (2041–2060), most cities will face decreasing precipitation, with reductions exceeding 60% in some months in northwestern regions. At the same time, some low-precipitation cities, such as Semnan, may experience increases during warm months. The largest changes—both increases and decreases—are projected for the warm season (late spring to late summer).A comparison of total annual precipitation during the observation and forecast periods confirmed a significant decline in all 12 cities, with the greatest decrease expected in Rasht and the smallest in Semnan. These results highlight temporal and spatial anomalies in precipitation, confirm the strong influence of climate change on the region, and underscore the need for effective water resource management and adaptation strategies.Climate change has led to alterations in the temporal and spatial distribution of precipitation across the globe. The present study aims to identify trends and project future precipitation in selected cities of northern Iran. To achieve this goal, the 30-year precipitation trend (1994–2023) for each city was first examined using the Mann-Kendall test and Sen’s slope estimator. In the second phase, precipitation was projected for the next 20 years (2040–2060) in the study area using the latest version of the LARS-WG8 weather generator under two scenarios—SSP2-4.5 and SSP5-8.5—with the ACCESS-ESM1-5 and CNRM-CM6-1 models. The outputs of both models were then evaluated and compared using validation indices (R², MAPE, and RMSE) to select the most suitable model. Trend analysis results indicated that over the past 30 years (1994–2023), the precipitation pattern in all stations in the region changed significantly, showing a decreasing trend at the 99% confidence level. This decrease was greater in the cities of Qazvin, Ardabil, and Zanjan, and less in Rasht and Gorgan. According to the projections from the two models, ACCESS-ESM1-5 and CNRM-CM6, over the next 20 years (2040–2060), the entire study area will face a decrease in precipitation in most months of the year. The decrease in precipitation in the northwest will be more than 60% in some months. Conversely, some cities will experience an increase in precipitation during the warm seasons, which will be more evident in low-precipitation cities such as Semnan. Finally, the comparison of the total annual precipitation of the observed and forecast periods confirmed a significant decrease in precipitation across the entire study area compared to the base period. The research findings indicate temporal and spatial anomalies in precipitation, confirm the effects of climate change on the region, and emphasize the need to adopt efficient management policies to optimize water resource consumption and develop strategies for adapting to climate change.Introduction Climate change has altered the temporal and spatial distribution of precipitation and its intensity throughout the world. Precipitation description, analysis, and modeling are among the most challenging issues in climate research, reflecting its complex and irregular temporal and spatial structure. In this regard, several modeling methods have been developed to deal with this complexity (Sánchez et al., 2011). A reduction in the number of rainy days and an increase in precipitation intensity over limited days can increase the probability of flooding in areas such as the coastal cities of the Caspian Sea, as well as cause soil erosion and reduce water infiltration into groundwater resources.Material and MethodsIn the present study, the trend and forecast of precipitation in selected cities in northern Iran (Ardabil, Tabriz, Bojnourd, Mashhad, Gorgan, Semnan, Zanjan, Tehran, Urmia, Gorgan, Rasht and, Sanandaj) were investigated. To analyzed the total annual precipitation, data from the last 30 years (1994-2023) were obtained from the Iranian Meteorological Organization, and Sen's slope estimator, a non-parametric method for estimating the slope of the regression line in trend analysis, was applied.Results and DiscussionAccording to the results of the trend analysis, during the statistical period (1994–2023), the Q statistic (Sen’s slope estimator) and Z (Mann-Kendall test) were negative for all stations, indicating that the total annual precipitation in all 12 cities showed a decreasing trend. The largest Q values were observed in Qazvin (-81.8), Ardabil (-63.8), and Zanjan (-45.8). The highest negative Z values occurred in Sanandaj (-4.62), Zanjan (-4.57), and Qazvin (-4.42), while the lowest was in Rasht (-2.98). Since the Z scores in all cities were less than -2.58, a significant downward trend in precipitation during the past 30 years is confirmed at the 99% confidence level.In the second part of the study, after validating the performance of LARS-WG8 in simulating base-period precipitation (1980–2010), the ACCESS-ESM1-5 and CNRM-CM6-1 models were used to predict future precipitation under the SSP2-4.5 and SSP5-8.5 scenarios. According to the results, Urmia is projected to experience decreased precipitation in all months under both models. Tehran, Tabriz, Ardabil, Gorgan, and Rasht will also face significant reductions in most months, with only slight increases (less than 20%) under some scenarios. Zanjan, Sanandaj, Qazvin, Mashhad, Bojnourd, and Semnan are expected to experience up to a 50% increase in precipitation in at least one late-spring or summer month. Overall, projections from both models suggest that all 12 cities will experience a significant reduction in precipitation compared to the long-term average during most months of the next 20 years.Finally, model accuracy was assessed using R², RMSE, and MAPE. Although both models showed relatively high error rates, each city exhibited higher accuracy under one model. Consequently, the CNRM model was selected for Sanandaj, Ardabil, Tehran, and Gorgan, while the ACCESS model was selected for the remaining cities.ConclusionThe aim of the present study was to forecast and analyze precipitation trends in selected cities of northern Iran using GCM models, in order to reveal the extent of climate change impacts on different climatic regions. Based on Sen’s slope estimator and Mann-Kendall tests, precipitation in all cities during the past 30 years (1994–2023) showed a significant decreasing trend at the 99% confidence level. The decrease was most pronounced in mountainous cities in the northwest (Qazvin, Ardabil, and Zanjan) and least in humid coastal cities (Rasht and Gorgan).Projections from the ACCESS and CNRM models suggest that precipitation patterns in the study area will undergo significant changes. Over the next 20 years (2041–2060), most cities will face decreasing precipitation, with reductions exceeding 60% in some months in northwestern regions. At the same time, some low-precipitation cities, such as Semnan, may experience increases during warm months. The largest changes—both increases and decreases—are projected for the warm season (late spring to late summer).A comparison of total annual precipitation during the observation and forecast periods confirmed a significant decline in all 12 cities, with the greatest decrease expected in Rasht and the smallest in Semnan. These results highlight temporal and spatial anomalies in precipitation, confirm the strong influence of climate change on the region, and underscore the need for effective water resource management and adaptation strategies.https://geoeh.um.ac.ir/article_46980_fe2e9cb2ea6cb28769e3c4947d156690.pdfFerdowsi University of MashhadJournal of Geography and Environmental Hazards2322-168214320250923General Characteristics of the Lightning Phenomenon in IranGeneral Characteristics of the Lightning Phenomenon in Iran1401654705910.22067/geoeh.2025.89265.1508FAHamzehAlizadehPh.D. Candidate in Climatology, University of Zanjan, Zanjan, Iran0000-0003-2425-5561HossienAsakerehProfessor in Climatology, Department of Geography, University of Zanjan, Zanjan, Iran0000-0001-7699-0547KoohzadRaispourAssistant Professor in Climatology, Department of Geography, University of Zanjan, Zanjan, Iran0000-0003-1508-9178Journal Article20240828Lightning is a prominent atmospheric phenomenon, characterized by the transfer and discharge of electricity between clouds and the Earth's surface. This process poses significant risks to both human safety and the environment, thereby necessitating a detailed investigation of its climatological features. In addition to the electrical transfer, lightning is also accompanied by intense sound and light. Cloud formation typically begins at altitudes above the condensation level, where water droplets form below the zero-degree isotherm, and ice crystals develop above it. As these water droplets and ice crystals grow, they descend at an approximate rate of 8 meters per second. If the downward velocity of the particles is slower than the upward movement of the cloud, the droplets and crystals are subjected to continuous updrafts and downdrafts, resulting in repeated collisions. This process leads to the accumulation of larger particles and the separation of electrical charges, with negative charges concentrating at the base of the cloud and positive charges accumulating at the top. This study examines the statistical and geostatistical characteristics of lightning frequency across Iran from 2000 to 2022, using data from 382 synoptic stations. Descriptive statistics, including the mean and coefficient of variation, were computed for lightning events, and their correlations with geographic variables such as latitude, longitude, and altitude were assessed. Regression analysis was employed to model these relationships. The results reveal that the spatial distribution of lightning frequency in Iran follows a stochastic pattern, exhibiting significant annual variability. Among the spatial variables analyzed, latitude and longitude showed a stronger correlation with annual lightning frequency than altitude.Introduction Lightning is a natural phenomenon that can be hazardous, claiming the lives of over two thousand people globally each year. It occurs predominantly in regions with variable climates and involves the transfer and discharge of electricity between clouds and the Earth's surface. This phenomenon has far-reaching consequences for both the environment and human society. For instance, lightning can ignite fires, cause financial losses, and result in fatalities when it strikes tall structures such as trees and buildings. It ranks second among atmospheric hazards in terms of mortality.The occurrence of lightning on Earth is closely linked to climatic conditions, particularly temporal and spatial variations in solar radiation. The highest global frequency of lightning is found in the tropical regions of the Americas, Africa, and the oceans. Approximately 78% of lightning events occur between latitudes 30°S and 30°N. Satellite data indicates that lightning frequency is generally higher in tropical and humid climates. In coastal areas, lightning events account for about 70% of all oceanic lightning occurrences, with a higher frequency observed on the eastern coasts of continents compared to the western coasts. Consequently, the spatial distribution of lightning demonstrates considerable variability.Lightning frequency tends to peak in the morning and afternoon, with a notable reduction during nighttime hours. Seasonal patterns also influence lightning occurrences, with the highest frequency generally observed in July. Studies conducted in Iran have identified diverse temporal and spatial patterns of lightning, with the highest frequency recorded in the northern regions of Khuzestan Province and the southern regions of Lorestan Province. In contrast, the central and flat interior areas exhibit lower lightning frequencies.Material and MethodsDataLightning frequency data from 2000 to 2022 were collected from 411 active synoptic stations across Iran. After reviewing the dataset, stations with insufficient records were excluded, leaving 382 stations with reliable data for analysis (Figure 1). Daily lightning event records were obtained from the Iranian Meteorological Organization (IRIMO) website (https://data.irimo.ir/withoutlogin/index.aspx)).An examination of station altitudinal distribution (Table 1) revealed that around 56% of stations (212) were located between 500 and 2000 meters, while only about 3% were situated in low-lying areas (–22 to 0 meters). The number of stations decreased at higher altitudes, with only one station located above 3000 meters.This study investigates the long-term (climatic) frequency of lightning and its relationship with geographic coordinates (longitude, latitude) and altitude. Descriptive statistics, including the annual mean and coefficient of variation, were computed to characterize lightning events, and their spatial distribution was mapped.Results and DiscussionGeneral CharacteristicsThe highest frequency of lightning was observed in northwestern Iran. Precipitation in this region is primarily driven by convective activity, orographic uplift, and local fronts. Additionally, temperature and pressure contrasts induced by topography contribute to the frequency of lightning events.In southern Iran, which has a hot and humid climate, the annual lightning frequency was found to be similar to that in the western regions. Stations in northern regions also recorded relatively high frequencies, consistent with their temperate and humid climate, which is conducive to lightning activity. The mean coefficient of variation across stations was approximately 25.5%. A value below 25.5 indicates relatively stable lightning occurrence, while higher values suggest more irregular patterns and strong year-to-year variability.Probability AnalysisThe highest probability of lightning occurrence was observed in the high and mountainous regions of the Zagros and Alborz mountain ranges. Factors contributing to this include the mountainous terrain, the frequent passage of air masses, and the uneven distribution of solar radiation on slopes, which promote atmospheric instability and convection.The western half of Iran, especially the northwest, lies along the path of various air masses (including westerlies, Mediterranean cyclones, and associated short waves) during the cold season, creating favorable conditions for thunderstorms. The northern regions also experience significant lightning activity due to the moisture from the Caspian Sea, considerable elevations, and the combined effects of migratory westerly systems and Siberian high-pressure systems. In addition to these regions, scattered areas across the country experience occasional lightning events.ConclusionLightning is a natural phenomenon that occurs worldwide, especially in regions with variable climates such as Iran. Analyzing data from 2000–2022 reveals that geographic coordinates and elevation have a minimal linear effect on the distribution and frequency of lightning events. The coefficient of variation maps highlight irregular lightning occurrences and significant annual variations across the country.Correlation analysis indicates weak linear relationships between geographic factors and lightning frequency. Latitude exhibits a slight positive correlation with lightning activity in the northern regions, while longitude shows a moderate negative correlation, with lightning frequency decreasing eastward. Overall, lightning in Iran follows a stochastic pattern, with no strong linear spatial association with geographic coordinates or altitude.Satellite-based monitoring, such as the Lightning Imaging Sensor (LIS) on the TRMM satellite, offers near-real-time and accurate coverage, complementing station-based measurements. Future studies should incorporate satellite data to more effectively capture the temporal and spatial distribution of lightning, thereby providing a comprehensive understanding of this phenomenon across Iran's diverse climatic regions.Lightning is a prominent atmospheric phenomenon, characterized by the transfer and discharge of electricity between clouds and the Earth's surface. This process poses significant risks to both human safety and the environment, thereby necessitating a detailed investigation of its climatological features. In addition to the electrical transfer, lightning is also accompanied by intense sound and light. Cloud formation typically begins at altitudes above the condensation level, where water droplets form below the zero-degree isotherm, and ice crystals develop above it. As these water droplets and ice crystals grow, they descend at an approximate rate of 8 meters per second. If the downward velocity of the particles is slower than the upward movement of the cloud, the droplets and crystals are subjected to continuous updrafts and downdrafts, resulting in repeated collisions. This process leads to the accumulation of larger particles and the separation of electrical charges, with negative charges concentrating at the base of the cloud and positive charges accumulating at the top. This study examines the statistical and geostatistical characteristics of lightning frequency across Iran from 2000 to 2022, using data from 382 synoptic stations. Descriptive statistics, including the mean and coefficient of variation, were computed for lightning events, and their correlations with geographic variables such as latitude, longitude, and altitude were assessed. Regression analysis was employed to model these relationships. The results reveal that the spatial distribution of lightning frequency in Iran follows a stochastic pattern, exhibiting significant annual variability. Among the spatial variables analyzed, latitude and longitude showed a stronger correlation with annual lightning frequency than altitude.Introduction Lightning is a natural phenomenon that can be hazardous, claiming the lives of over two thousand people globally each year. It occurs predominantly in regions with variable climates and involves the transfer and discharge of electricity between clouds and the Earth's surface. This phenomenon has far-reaching consequences for both the environment and human society. For instance, lightning can ignite fires, cause financial losses, and result in fatalities when it strikes tall structures such as trees and buildings. It ranks second among atmospheric hazards in terms of mortality.The occurrence of lightning on Earth is closely linked to climatic conditions, particularly temporal and spatial variations in solar radiation. The highest global frequency of lightning is found in the tropical regions of the Americas, Africa, and the oceans. Approximately 78% of lightning events occur between latitudes 30°S and 30°N. Satellite data indicates that lightning frequency is generally higher in tropical and humid climates. In coastal areas, lightning events account for about 70% of all oceanic lightning occurrences, with a higher frequency observed on the eastern coasts of continents compared to the western coasts. Consequently, the spatial distribution of lightning demonstrates considerable variability.Lightning frequency tends to peak in the morning and afternoon, with a notable reduction during nighttime hours. Seasonal patterns also influence lightning occurrences, with the highest frequency generally observed in July. Studies conducted in Iran have identified diverse temporal and spatial patterns of lightning, with the highest frequency recorded in the northern regions of Khuzestan Province and the southern regions of Lorestan Province. In contrast, the central and flat interior areas exhibit lower lightning frequencies.Material and MethodsDataLightning frequency data from 2000 to 2022 were collected from 411 active synoptic stations across Iran. After reviewing the dataset, stations with insufficient records were excluded, leaving 382 stations with reliable data for analysis (Figure 1). Daily lightning event records were obtained from the Iranian Meteorological Organization (IRIMO) website (https://data.irimo.ir/withoutlogin/index.aspx)).An examination of station altitudinal distribution (Table 1) revealed that around 56% of stations (212) were located between 500 and 2000 meters, while only about 3% were situated in low-lying areas (–22 to 0 meters). The number of stations decreased at higher altitudes, with only one station located above 3000 meters.This study investigates the long-term (climatic) frequency of lightning and its relationship with geographic coordinates (longitude, latitude) and altitude. Descriptive statistics, including the annual mean and coefficient of variation, were computed to characterize lightning events, and their spatial distribution was mapped.Results and DiscussionGeneral CharacteristicsThe highest frequency of lightning was observed in northwestern Iran. Precipitation in this region is primarily driven by convective activity, orographic uplift, and local fronts. Additionally, temperature and pressure contrasts induced by topography contribute to the frequency of lightning events.In southern Iran, which has a hot and humid climate, the annual lightning frequency was found to be similar to that in the western regions. Stations in northern regions also recorded relatively high frequencies, consistent with their temperate and humid climate, which is conducive to lightning activity. The mean coefficient of variation across stations was approximately 25.5%. A value below 25.5 indicates relatively stable lightning occurrence, while higher values suggest more irregular patterns and strong year-to-year variability.Probability AnalysisThe highest probability of lightning occurrence was observed in the high and mountainous regions of the Zagros and Alborz mountain ranges. Factors contributing to this include the mountainous terrain, the frequent passage of air masses, and the uneven distribution of solar radiation on slopes, which promote atmospheric instability and convection.The western half of Iran, especially the northwest, lies along the path of various air masses (including westerlies, Mediterranean cyclones, and associated short waves) during the cold season, creating favorable conditions for thunderstorms. The northern regions also experience significant lightning activity due to the moisture from the Caspian Sea, considerable elevations, and the combined effects of migratory westerly systems and Siberian high-pressure systems. In addition to these regions, scattered areas across the country experience occasional lightning events.ConclusionLightning is a natural phenomenon that occurs worldwide, especially in regions with variable climates such as Iran. Analyzing data from 2000–2022 reveals that geographic coordinates and elevation have a minimal linear effect on the distribution and frequency of lightning events. The coefficient of variation maps highlight irregular lightning occurrences and significant annual variations across the country.Correlation analysis indicates weak linear relationships between geographic factors and lightning frequency. Latitude exhibits a slight positive correlation with lightning activity in the northern regions, while longitude shows a moderate negative correlation, with lightning frequency decreasing eastward. Overall, lightning in Iran follows a stochastic pattern, with no strong linear spatial association with geographic coordinates or altitude.Satellite-based monitoring, such as the Lightning Imaging Sensor (LIS) on the TRMM satellite, offers near-real-time and accurate coverage, complementing station-based measurements. Future studies should incorporate satellite data to more effectively capture the temporal and spatial distribution of lightning, thereby providing a comprehensive understanding of this phenomenon across Iran's diverse climatic regions.https://geoeh.um.ac.ir/article_47059_b7e21bd47130fce0c917dda4c3e6e30f.pdfFerdowsi University of MashhadJournal of Geography and Environmental Hazards2322-168214320250923Spatio-Temporal Analysis of Earthquake Occurrences in Iran (1996–2024): A Vulnerability Assessment with a Focus on Urban AreasSpatio-Temporal Analysis of Earthquake Occurrences in Iran (1996–2024): A Vulnerability Assessment with a Focus on Urban Areas1661894708810.22067/geoeh.2025.92804.1562FARoyaMoghabeliPh.D. Student of Geography and Urban Planning, Artment of Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran0000-0003-0193-2427FaezehBahadoriPh.D. Student of Geography and Urban Planning, Artment of Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran0009-0005-0292-2556AlirezaMohammadiProfessor, Department of Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran0000-0002-3327-0942Journal Article20250328Earthquakes are among the most destructive natural hazards worldwide, causing extensive damage to human societies each year. Spatial and temporal analysis of this phenomenon plays a key role in identifying high-risk areas and guiding crisis management strategies. Due to its location on the active Alpine-Himalayan seismic belt, Iran is one of the most earthquake-prone countries in the world, with over 1,150 fatal earthquakes recorded in the past century. This study aimed to identify the spatial and temporal patterns of earthquakes in Iran from 1996 to 2024 using data from more than 10,000 seismic events (M ≥ 4), analyzed using GIS tools. Methods employed included Kernel Density Estimation, Getis-Ord Gi* hot spot analysis, and Local Moran’s I index. Results revealed that the highest concentrations and clustering of earthquakes were observed in the western and southwestern regions, especially along the Zagros and Alborz faults. Furthermore, 59% of Iranian cities lie within 14 km of earthquake epicenters, with cities such as Zarand, Khormuj, and Kazeroon facing very high seismic risk. The study's innovation lies in its nationwide scale and the integration of extensive datasets with advanced spatial models. The findings provide critical insights to support urban retrofitting, disaster risk reduction policies, and enhanced resilience in earthquake-prone areas.IntroductionEarthquakes are among the most devastating natural disasters, causing significant human and financial losses worldwide. Iran, located in the seismically active Alpine-Himalayan belt, has experienced numerous destructive earthquakes due to its complex tectonic structure. The country's vulnerability to earthquakes is further exacerbated by rapid urbanization, poor building standards, and a high concentration of population in high-risk areas.Since 1996, more than 10,000 earthquakes (magnitude ≥ 4.0) have been recorded in Iran, with a significant portion occurring in the Zagros, Alborz, and eastern regions. The spatial and temporal analysis of earthquakes is essential for understanding their patterns and developing effective risk reduction strategies. Despite extensive studies on earthquake hazards, comprehensive research integrating spatial and temporal trends at a national scale remains scarce. This study seeks to address this gap by analyzing the spatial distribution and temporal occurrence of earthquakes in Iran from 1996 to 2024.The key objectives of this research include:Identifying high-risk seismic zones based on historical earthquake data.Analyzing the relationship between seismic events and active fault lines.Assessing the vulnerability of urban areas to earthquake hazards.Providing insights for earthquake risk mitigation and urban planning policies.Using GIS-based spatial analysis methods, this study evaluates earthquake clusters, density patterns, and their correlation with urban centers and fault zones. The findings contribute to a better understanding of seismic hazards in Iran and offer valuable recommendations for improving disaster preparedness and resilience in urban areas.Material and Methods Earthquakes are among the most devastating natural disasters, causing significant human and financial losses. Iran, located in the Alpine-Himalayan belt, has experienced frequent seismic activity due to its complex tectonic structure. Since 1996, more than 10,000 earthquakes (magnitude ≥ 4.0) have been recorded, mostly in the Zagros, Alborz, and eastern regions. Rapid urbanization, poor building standards, and dense populations in high-risk areas exacerbate the vulnerability. This study analyzes the spatial and temporal distribution of earthquakes in Iran (1996–2024) to identify high-risk zones, assess urban vulnerability, and provide insights for risk mitigation. Using GIS-based methods, we evaluate earthquake clusters, density patterns, and their correlation with fault zones.This research adopts a quantitative approach using spatial analysis techniques. Earthquake data (magnitude ≥ 4.0) were obtained from the Iranian Seismological Center and processed in ArcGIS. Fault line maps and urban population data were integrated into a GIS framework. Spatial methods such as Kernel Density Estimation (KDE) identified high-risk zones, Getis-Ord Gi* detected seismic clusters, and Buffer Analysis assessed city proximity to faults. Moran’s, I measured clustering patterns. The results highlight seismic hazard variations and provide guidance for urban planning strategies to enhance disaster resilience.Results and Discussion The results indicate that seismic activity is highly concentrated in western, northwestern, and southwestern Iran, particularly along the Zagros and Alborz fault lines. Kernel Density Estimation (KDE) revealed that the highest earthquake densities occurres in the provinces of Hormozgan, Kerman, Fars, and Bushehr. Getis-Ord Gi* analysis identified significant seismic hot spots in these regions, confirming their high-risk status. Buffer Analysis showed that nearly 59% of major Iranian cities are within 40 km of an earthquake epicenter, with cities like Zarand, Khormuj, Ravar, and Kazeroon being particularly vulnerable. Additionally, 73% of recorded earthquakes occurred at depths of less than 15 km, increasing the likelihood of severe damage in urban areas.The findings align with previous studies, which highlight the active seismicity of western and southern Iran due to major fault systems. However, some discrepancies were observed, such as the lower-than-expected earthquake density in northeastern Iran, possibly due to variations in data collection periods and methodologies. These results emphasize the urgent need for stricter construction regulations, seismic hazard zoning, and the integration of GIS-based monitoring systems to enhance disaster preparedness and resilience.Future research should incorporate geotechnical factors, historical seismic patterns, and predictive modeling using artificial intelligence for improved risk assessment and mitigation strategies.Conclusion This study analyzed the spatial and temporal distribution of earthquakes in Iran from 1996 to 2024 (Gregorian calendar) to identify high-risk zones and assess urban vulnerability. The findings revealed that seismic activity is concentrated in western, northwestern, and southwestern regions, particularly along the Zagros and Alborz fault lines. Approximately 59% of major Iranian cities are within 40 km of an earthquake epicenter, increasing their risk of damage. Additionally, the majority of earthquakes occurred at shallow depths, further intensifying their impact on urban areas.The results underscore the necessity for stricter construction regulations, enhanced seismic hazard zoning, and the integration of GIS-based monitoring systems to improve disaster preparedness. Implementing early warning systems, strengthening infrastructure, and promoting public awareness campaigns are crucial steps toward reducing earthquake risks.Future research should focus on incorporating geotechnical assessments, historical seismic trends, and predictive modeling using artificial intelligence to enhance earthquake hazard mitigation strategies. By adopting a data-driven approach, policymakers and urban planners can develop more effective disaster risk management plans, ultimately increasing the resilience of Iranian cities against seismic hazards. Earthquakes are among the most destructive natural hazards worldwide, causing extensive damage to human societies each year. Spatial and temporal analysis of this phenomenon plays a key role in identifying high-risk areas and guiding crisis management strategies. Due to its location on the active Alpine-Himalayan seismic belt, Iran is one of the most earthquake-prone countries in the world, with over 1,150 fatal earthquakes recorded in the past century. This study aimed to identify the spatial and temporal patterns of earthquakes in Iran from 1996 to 2024 using data from more than 10,000 seismic events (M ≥ 4), analyzed using GIS tools. Methods employed included Kernel Density Estimation, Getis-Ord Gi* hot spot analysis, and Local Moran’s I index. Results revealed that the highest concentrations and clustering of earthquakes were observed in the western and southwestern regions, especially along the Zagros and Alborz faults. Furthermore, 59% of Iranian cities lie within 14 km of earthquake epicenters, with cities such as Zarand, Khormuj, and Kazeroon facing very high seismic risk. The study's innovation lies in its nationwide scale and the integration of extensive datasets with advanced spatial models. The findings provide critical insights to support urban retrofitting, disaster risk reduction policies, and enhanced resilience in earthquake-prone areas.IntroductionEarthquakes are among the most devastating natural disasters, causing significant human and financial losses worldwide. Iran, located in the seismically active Alpine-Himalayan belt, has experienced numerous destructive earthquakes due to its complex tectonic structure. The country's vulnerability to earthquakes is further exacerbated by rapid urbanization, poor building standards, and a high concentration of population in high-risk areas.Since 1996, more than 10,000 earthquakes (magnitude ≥ 4.0) have been recorded in Iran, with a significant portion occurring in the Zagros, Alborz, and eastern regions. The spatial and temporal analysis of earthquakes is essential for understanding their patterns and developing effective risk reduction strategies. Despite extensive studies on earthquake hazards, comprehensive research integrating spatial and temporal trends at a national scale remains scarce. This study seeks to address this gap by analyzing the spatial distribution and temporal occurrence of earthquakes in Iran from 1996 to 2024.The key objectives of this research include:Identifying high-risk seismic zones based on historical earthquake data.Analyzing the relationship between seismic events and active fault lines.Assessing the vulnerability of urban areas to earthquake hazards.Providing insights for earthquake risk mitigation and urban planning policies.Using GIS-based spatial analysis methods, this study evaluates earthquake clusters, density patterns, and their correlation with urban centers and fault zones. The findings contribute to a better understanding of seismic hazards in Iran and offer valuable recommendations for improving disaster preparedness and resilience in urban areas.Material and Methods Earthquakes are among the most devastating natural disasters, causing significant human and financial losses. Iran, located in the Alpine-Himalayan belt, has experienced frequent seismic activity due to its complex tectonic structure. Since 1996, more than 10,000 earthquakes (magnitude ≥ 4.0) have been recorded, mostly in the Zagros, Alborz, and eastern regions. Rapid urbanization, poor building standards, and dense populations in high-risk areas exacerbate the vulnerability. This study analyzes the spatial and temporal distribution of earthquakes in Iran (1996–2024) to identify high-risk zones, assess urban vulnerability, and provide insights for risk mitigation. Using GIS-based methods, we evaluate earthquake clusters, density patterns, and their correlation with fault zones.This research adopts a quantitative approach using spatial analysis techniques. Earthquake data (magnitude ≥ 4.0) were obtained from the Iranian Seismological Center and processed in ArcGIS. Fault line maps and urban population data were integrated into a GIS framework. Spatial methods such as Kernel Density Estimation (KDE) identified high-risk zones, Getis-Ord Gi* detected seismic clusters, and Buffer Analysis assessed city proximity to faults. Moran’s, I measured clustering patterns. The results highlight seismic hazard variations and provide guidance for urban planning strategies to enhance disaster resilience.Results and Discussion The results indicate that seismic activity is highly concentrated in western, northwestern, and southwestern Iran, particularly along the Zagros and Alborz fault lines. Kernel Density Estimation (KDE) revealed that the highest earthquake densities occurres in the provinces of Hormozgan, Kerman, Fars, and Bushehr. Getis-Ord Gi* analysis identified significant seismic hot spots in these regions, confirming their high-risk status. Buffer Analysis showed that nearly 59% of major Iranian cities are within 40 km of an earthquake epicenter, with cities like Zarand, Khormuj, Ravar, and Kazeroon being particularly vulnerable. Additionally, 73% of recorded earthquakes occurred at depths of less than 15 km, increasing the likelihood of severe damage in urban areas.The findings align with previous studies, which highlight the active seismicity of western and southern Iran due to major fault systems. However, some discrepancies were observed, such as the lower-than-expected earthquake density in northeastern Iran, possibly due to variations in data collection periods and methodologies. These results emphasize the urgent need for stricter construction regulations, seismic hazard zoning, and the integration of GIS-based monitoring systems to enhance disaster preparedness and resilience.Future research should incorporate geotechnical factors, historical seismic patterns, and predictive modeling using artificial intelligence for improved risk assessment and mitigation strategies.Conclusion This study analyzed the spatial and temporal distribution of earthquakes in Iran from 1996 to 2024 (Gregorian calendar) to identify high-risk zones and assess urban vulnerability. The findings revealed that seismic activity is concentrated in western, northwestern, and southwestern regions, particularly along the Zagros and Alborz fault lines. Approximately 59% of major Iranian cities are within 40 km of an earthquake epicenter, increasing their risk of damage. Additionally, the majority of earthquakes occurred at shallow depths, further intensifying their impact on urban areas.The results underscore the necessity for stricter construction regulations, enhanced seismic hazard zoning, and the integration of GIS-based monitoring systems to improve disaster preparedness. Implementing early warning systems, strengthening infrastructure, and promoting public awareness campaigns are crucial steps toward reducing earthquake risks.Future research should focus on incorporating geotechnical assessments, historical seismic trends, and predictive modeling using artificial intelligence to enhance earthquake hazard mitigation strategies. By adopting a data-driven approach, policymakers and urban planners can develop more effective disaster risk management plans, ultimately increasing the resilience of Iranian cities against seismic hazards. https://geoeh.um.ac.ir/article_47088_31b291792c6140b04d1d5d5a8ae4bcb1.pdfFerdowsi University of MashhadJournal of Geography and Environmental Hazards2322-168214320250923Dam Construction in the Light of Green Criminology: A Content Analysis of the Novel After the CloudDam Construction in the Light of Green Criminology: A Content Analysis of the Novel After the Cloud1902104708710.22067/geoeh.2025.93741.1576FAHamid RezaDanesh NariAssistant Professor, Department of Criminal Law and Criminology, Faculty of Law and Political Science, Ferdowsi University of Mashhad, Mashhad, Iran0000-0003-2676-0422AbolfazlSherafatiM.Sc. Student, Department of Criminal Law and Criminology, Faculty of Law and Political Science, Ferdowsi University of Mashhad, Mashhad, IranJournal Article20250527Environmental issues have become one of the major concerns of various sciences in recent decades. Criminology, as a branch of the social sciences, is not indifferent to these issues. Contemporary critical criminology has introduced new topics to the field, one of which is environmental damage and protection. The importance of these issues is so great that it has led to the formation of a new branch in criminology, called green criminology. In this research, using the qualitative content analysis method, the novel After the Cloud by Babak Zamani was analyzed based on the theory of green criminology, with an emphasis on the environmental damage caused by dam construction. The results of this study indicate that dam construction projects, both during construction and afterwards, have negative environmental consequences that can result in ecosystem destruction. Dam construction can also lead to forced displacement due to rising water levels and the submergence of indigenous peoples' habitats. In this context, the lack of government support makes indigenous people the main victims of unregulated dam construction.IntroductionThe theory of green criminology was first proposed by Michael Lynch in 1990. In an article titled “The Greening of Criminology,” he focused on environmental crimes and analyzed them with a critical eye. Environmental crimes are a broad concept that, in addition to economic damage, also violate the right to a healthy environment. Theorists in this field believe that because environmental crimes are often accompanied by widespread damage and sometimes restoration to the original state is impossible, the best solution is prevention and proactive measures.One of the important topics in the analysis of environmental crimes is the issue of political economy, which emphasizes two areas. First, it examines the role and activities of the state that facilitate or create conditions for environmental destruction and damage. These issues are related to public policymaking and the constant intervention of governments. Second, it explores the decisive role of capitalism in production and its methods, which are in conflict with the environment. Therefore, environmental crimes are increasingly associated with the ideology of capitalism. For this reason, green criminology has a pessimistic view of capitalism, consumer culture, and social contradictions, reflecting its critical and Marxist nature.The development and operation of hydroelectric power plants and large dams are often accompanied by serious and widespread social consequences that are rarely properly identified or assessed. In many cases, responsible institutions do not adequately address the negative impacts of these projects on the lives of local communities. Since the 1970s, awareness of the environmental, economic, and social impacts of dams has increased. The World Commission on Dams (WCD) warned more than two decades ago that the construction of large dams without sufficient attention to social consequences has caused widespread damage, including the displacement of millions of people, loss of livelihoods, and deterioration in the quality of life. Moreover, in most cases, compensation for indigenous people has been inadequate, and efforts to restore their living conditions have failed.This research aims to examine and evaluate the novel After the Cloud in the light of green criminology theory, using qualitative content analysis. The novel, written by Babak Zamani in the style of social realism, primarily explores the consequences of dam construction. The main research question is: How are environmental damage and the negative consequences of dam construction represented in this realistic novel? What roles do power and wealth groups play in the occurrence and escalation of environmental crimes?Material and MethodsThis study employs qualitative content analysis to examine data related to critical criminology in the novel After the Cloud. Qualitative content analysis is a suitable method for analyzing textual data in large volumes and in specific contexts. Through this method, the most important categories of the text are identified, and its hidden meanings are uncovered. By coding and categorizing the data, theoretical concepts can be tested and their validity assessed. Since the success of qualitative content analysis depends on the precise and purposeful selection of categories, which must align with the hypotheses and theoretical framework of the research, the categories in this study were chosen to directly correspond to the concepts and concerns of green criminology. Each category reflects one of the theoretical dimensions of this approach in explaining environmental crimes and damage.Results and DiscussionThe research findings are presented under three categories: (1) dam construction in the light of green criminology, (2) environmental crimes of the powerful, and (3) victimization of indigenous people, followed by (4) the sociological harms of dam construction projects.Dam projects clearly demonstrate human rights violations against indigenous and local communities. These projects, ostensibly undertaken for the development and exploitation of water resources and energy production, often violate the fundamental rights of affected populations. The loss of agricultural land, homes, and water resources that generations have relied on to sustain their livelihoods is just one example of these violations. Indigenous and local communities are forcibly displaced, separated from their natural environment, and stripped of their cultural and social identity. Furthermore, inadequate attention to their post-displacement well-being—such as the absence of jobs, education, and social infrastructure—creates conditions in which basic human rights are violated. In After the Cloud, dam construction is presented by the company as a project for development, but in reality, it results in environmental destruction, mountain explosions, and the burial of plant species and agricultural lands.A common shortcoming of dam projects is the lack of commitment by government officials to the resettlement of displaced people. Research shows that many villagers forcibly displaced by dam construction emphasize the role of government officials in their loss of capital and property. The institutional structure of these projects has a serious impact on how resettlement and other development-related issues are addressed. In After the Cloud, one of the author’s main concerns is the lack of accountability of companies and the government. The irreparable damage caused to the natural environment and residents goes largely unanswered, and no institution holds companies accountable.In green criminology, the concept of harm is also central. It goes beyond the violation of official laws to include socially harmful practices, even if the law does not classify them as crimes. In After the Cloud, social harms appear in various forms. The dam projects in this novel are not limited to environmental destruction; they also reveal widespread social harms. The lives of local people, who had developed their land and gardens for years, are suddenly submerged, and their lifelong efforts destroyed. The jobs promised are short-term, unstable, and degrading, undermining the future of the youth in these regions. Promises of employment and development soon give way to unemployment, poverty, forced migration, and family collapse.ConclusionThis novel helps readers better understand the concept of state and corporate crimes in a critical sense—by drawing attention to harms that are ignored in the official discourse of society. In this way, it expands the perspective on crime from its traditional and narrow definition to a broader, critical one in which crime includes all harmful practices of powerful groups, regardless of whether they are criminalized by law.In contemporary capitalism, the close relationship between large corporations and governments has created a cycle of environmental crimes in which the economic interests of capitalists take precedence over human rights and the environment. This power structure enables corporations to implement projects such as the construction of large dams with government support and permits. Although these projects are justified by goals such as energy supply, economic development, and natural resource management, in practice they have devastating consequences for nature and human societies, particularly indigenous peoples.Environmental issues have become one of the major concerns of various sciences in recent decades. Criminology, as a branch of the social sciences, is not indifferent to these issues. Contemporary critical criminology has introduced new topics to the field, one of which is environmental damage and protection. The importance of these issues is so great that it has led to the formation of a new branch in criminology, called green criminology. In this research, using the qualitative content analysis method, the novel After the Cloud by Babak Zamani was analyzed based on the theory of green criminology, with an emphasis on the environmental damage caused by dam construction. The results of this study indicate that dam construction projects, both during construction and afterwards, have negative environmental consequences that can result in ecosystem destruction. Dam construction can also lead to forced displacement due to rising water levels and the submergence of indigenous peoples' habitats. In this context, the lack of government support makes indigenous people the main victims of unregulated dam construction.IntroductionThe theory of green criminology was first proposed by Michael Lynch in 1990. In an article titled “The Greening of Criminology,” he focused on environmental crimes and analyzed them with a critical eye. Environmental crimes are a broad concept that, in addition to economic damage, also violate the right to a healthy environment. Theorists in this field believe that because environmental crimes are often accompanied by widespread damage and sometimes restoration to the original state is impossible, the best solution is prevention and proactive measures.One of the important topics in the analysis of environmental crimes is the issue of political economy, which emphasizes two areas. First, it examines the role and activities of the state that facilitate or create conditions for environmental destruction and damage. These issues are related to public policymaking and the constant intervention of governments. Second, it explores the decisive role of capitalism in production and its methods, which are in conflict with the environment. Therefore, environmental crimes are increasingly associated with the ideology of capitalism. For this reason, green criminology has a pessimistic view of capitalism, consumer culture, and social contradictions, reflecting its critical and Marxist nature.The development and operation of hydroelectric power plants and large dams are often accompanied by serious and widespread social consequences that are rarely properly identified or assessed. In many cases, responsible institutions do not adequately address the negative impacts of these projects on the lives of local communities. Since the 1970s, awareness of the environmental, economic, and social impacts of dams has increased. The World Commission on Dams (WCD) warned more than two decades ago that the construction of large dams without sufficient attention to social consequences has caused widespread damage, including the displacement of millions of people, loss of livelihoods, and deterioration in the quality of life. Moreover, in most cases, compensation for indigenous people has been inadequate, and efforts to restore their living conditions have failed.This research aims to examine and evaluate the novel After the Cloud in the light of green criminology theory, using qualitative content analysis. The novel, written by Babak Zamani in the style of social realism, primarily explores the consequences of dam construction. The main research question is: How are environmental damage and the negative consequences of dam construction represented in this realistic novel? What roles do power and wealth groups play in the occurrence and escalation of environmental crimes?Material and MethodsThis study employs qualitative content analysis to examine data related to critical criminology in the novel After the Cloud. Qualitative content analysis is a suitable method for analyzing textual data in large volumes and in specific contexts. Through this method, the most important categories of the text are identified, and its hidden meanings are uncovered. By coding and categorizing the data, theoretical concepts can be tested and their validity assessed. Since the success of qualitative content analysis depends on the precise and purposeful selection of categories, which must align with the hypotheses and theoretical framework of the research, the categories in this study were chosen to directly correspond to the concepts and concerns of green criminology. Each category reflects one of the theoretical dimensions of this approach in explaining environmental crimes and damage.Results and DiscussionThe research findings are presented under three categories: (1) dam construction in the light of green criminology, (2) environmental crimes of the powerful, and (3) victimization of indigenous people, followed by (4) the sociological harms of dam construction projects.Dam projects clearly demonstrate human rights violations against indigenous and local communities. These projects, ostensibly undertaken for the development and exploitation of water resources and energy production, often violate the fundamental rights of affected populations. The loss of agricultural land, homes, and water resources that generations have relied on to sustain their livelihoods is just one example of these violations. Indigenous and local communities are forcibly displaced, separated from their natural environment, and stripped of their cultural and social identity. Furthermore, inadequate attention to their post-displacement well-being—such as the absence of jobs, education, and social infrastructure—creates conditions in which basic human rights are violated. In After the Cloud, dam construction is presented by the company as a project for development, but in reality, it results in environmental destruction, mountain explosions, and the burial of plant species and agricultural lands.A common shortcoming of dam projects is the lack of commitment by government officials to the resettlement of displaced people. Research shows that many villagers forcibly displaced by dam construction emphasize the role of government officials in their loss of capital and property. The institutional structure of these projects has a serious impact on how resettlement and other development-related issues are addressed. In After the Cloud, one of the author’s main concerns is the lack of accountability of companies and the government. The irreparable damage caused to the natural environment and residents goes largely unanswered, and no institution holds companies accountable.In green criminology, the concept of harm is also central. It goes beyond the violation of official laws to include socially harmful practices, even if the law does not classify them as crimes. In After the Cloud, social harms appear in various forms. The dam projects in this novel are not limited to environmental destruction; they also reveal widespread social harms. The lives of local people, who had developed their land and gardens for years, are suddenly submerged, and their lifelong efforts destroyed. The jobs promised are short-term, unstable, and degrading, undermining the future of the youth in these regions. Promises of employment and development soon give way to unemployment, poverty, forced migration, and family collapse.ConclusionThis novel helps readers better understand the concept of state and corporate crimes in a critical sense—by drawing attention to harms that are ignored in the official discourse of society. In this way, it expands the perspective on crime from its traditional and narrow definition to a broader, critical one in which crime includes all harmful practices of powerful groups, regardless of whether they are criminalized by law.In contemporary capitalism, the close relationship between large corporations and governments has created a cycle of environmental crimes in which the economic interests of capitalists take precedence over human rights and the environment. This power structure enables corporations to implement projects such as the construction of large dams with government support and permits. Although these projects are justified by goals such as energy supply, economic development, and natural resource management, in practice they have devastating consequences for nature and human societies, particularly indigenous peoples.https://geoeh.um.ac.ir/article_47087_e8a0ec28fc4273aa546f3abb6a3c24d9.pdfFerdowsi University of MashhadJournal of Geography and Environmental Hazards2322-168214320250923Analysis of Factors Affecting Urban Environmental Pollution in Coastal Areas (Bandar Anzali, Rezvanshahr and Astara) Using (AHP and RF) ModelsAnalysis of Factors Affecting Urban Environmental Pollution in Coastal Areas (Bandar Anzali, Rezvanshahr and Astara) Using (AHP and RF) Models2112344676510.22067/geoeh.2025.92413.1554FAZahraPourmoghadamGraduate of Environmental Hazards, Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran0009-0004-9409-8412MojtabaYamaniProfessor, Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran0000-0002-2042-7365MansourJafar BigloAssociate Professor, Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran0000-0001-9745-2318Journal Article20250301Urban environmental pollution in coastal areas significantly impacts water and soil quality, biodiversity, public health, and tourism activities. This study aims to identify and map pollution risk zones along the coasts of Anzali, Rezvanshahr, and Astara using a hybrid approach combining the Analytical Hierarchy Process (AHP) and the Random Forest (RF) machine learning algorithm. Fourteen critical criteria—including urban wastewater, population, human activity, coastal degradation, natural attractions, and tourism infrastructure—were selected from an initial set of 24 indicators through comparative analysis. Using expert judgment, the relative weights of each factor were calculated in Expert Choice software, and GIS-based spatial layers were generated in ArcGIS Pro. The pollution risk map was created by integrating the weighted layers. The RF model was trained using 70% of the high-risk zones as training data and 30% for testing. Model validation using ROC–AUC analysis showed excellent accuracy for the RF model (AUC = 0.97). The results revealed that Anzali County had the highest proportion of very high-risk zones, while Rezvanshahr and Astara also exhibited significant high-risk areas. The findings highlight the combined impact of human and natural drivers on coastal pollution and offer valuable insights for environmental management, spatial planning, and policy interventions to mitigate future risks.Introduction Coastal areas require special attention due to the vulnerability of ecosystems and the dependence of human activities on them. Today, beaches have become one of the most popular destinations for tourists, and the presence of various coastal features creates diverse potentials for attracting visitors. Tourism, as a rapidly growing phenomenon, has become one of the largest industries in the world. The development of tourism and related recreational activities has a significant impact on the development patterns of regions and helps provide direct and indirect economic, social, cultural, and environmental benefits to host communities. However, at the same time, this development raises concerns and threats regarding environmental degradation and the destruction of the natural, historical, and cultural heritage of local residents. In fact, mass tourism does not equate to cleanliness or environmental sustainability. In this regard, some of the most significant impacts of tourism-related activities on the physical coastal environment include soil erosion, landslides, water quality degradation, shoreline alteration, and litter pollution. Litter pollution has become an undeniable threat to the sustainability of coastal ecosystems, and the threat posed by non-biodegradable plastic waste to coastal environments is increasingly evident. Pollution in the Caspian Sea poses a serious threat to aquatic life and human health. Unfortunately, coastal zones and estuaries are highly exposed to pollution, and this process endangers the survival of marine species. Urban environmental pollution in coastal areas can lead to a decline in water and soil quality, a reduction in biodiversity, health problems, and negative impacts on tourism and recreational activities.Material and MethodsThe main objective of this study is to identify and zone the coastal areas of the three counties of Anzali, Rezvanshahr, and Astara in terms of urban environmental pollution risk using the Analytic Hierarchy Process (AHP) and the Random Forest (RF) machine learning algorithm. In this regard, effective criteria were first identified. From an initial set of 24 criteria, 14 were selected: natural attractiveness, coastal degradation, river, road, human activity, urban drainage, population, urban waste, protection and management, facilities, security, natural landscapes, hotels, and the influence of tourism on population. Information layers for these were prepared in the ArcGIS Pro environment. Using the AHP method, the relative impact of each criterion was determined, and 70% of the high-risk areas were used as training data and 30% as test data for training the Random Forest algorithm. The predictive accuracy of the model results was evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC).Results and DiscussionThe results of validating the zoning maps using test data showed that the accuracy of the analytic hierarchy process and random forest models was 0.812 and 0.97, respectively. These results show that the random forest model has high accuracy in zoning coastal pollution. The results also show that the security criterion is the most significant in training the machine learning algorithm.The results of coastal zone pollution zoning in the analytic hierarchy process and random forest model showed that in Anzali County, 0.82% and 10.10% were in the very low-risk class, 0.55% and 16.55% in the low-risk class, 0.6% and 0.36% in the medium-risk class, 73.89% and 27.35% in the high-risk class, and 20.6% and 24.9% in the very high-risk class. In Rezvanshahr County, 0.57% and 12.55% were in the very low-risk class, 7.09% and 28.71% in the low-risk class, 21.27% and 22.68% in the medium-risk class, 66.64% and 98% in the high-risk class, and 0.45% and 0% in the very high-risk class. In Astara County, 0.02% and 26.73% were in the very low-risk class, 6.30% and 23.21% in the low-risk class, 23.87% and 31.20% in the medium-risk class, and 6.44% and 16.51% in the high-risk class.Conclusion Pollution from industrial and domestic wastewater entering the coastal waters is one of the major challenges on the northern coasts of Iran. In Bandar Anzali, this problem has been exacerbated due to the lack of adequate wastewater management infrastructure and poor enforcement of environmental laws. The general conclusion for the three cities of Astara, Bandar Anzali, and Rezvanshahr shows that all three regions face specific environmental pollution challenges caused by a combination of natural and human factors. Managers and planners can effectively reduce the risks of pollution in coastal areas by using the results of this research. Furthermore, these results will help facilitate the development and implementation of practical solutions.Urban environmental pollution in coastal areas significantly impacts water and soil quality, biodiversity, public health, and tourism activities. This study aims to identify and map pollution risk zones along the coasts of Anzali, Rezvanshahr, and Astara using a hybrid approach combining the Analytical Hierarchy Process (AHP) and the Random Forest (RF) machine learning algorithm. Fourteen critical criteria—including urban wastewater, population, human activity, coastal degradation, natural attractions, and tourism infrastructure—were selected from an initial set of 24 indicators through comparative analysis. Using expert judgment, the relative weights of each factor were calculated in Expert Choice software, and GIS-based spatial layers were generated in ArcGIS Pro. The pollution risk map was created by integrating the weighted layers. The RF model was trained using 70% of the high-risk zones as training data and 30% for testing. Model validation using ROC–AUC analysis showed excellent accuracy for the RF model (AUC = 0.97). The results revealed that Anzali County had the highest proportion of very high-risk zones, while Rezvanshahr and Astara also exhibited significant high-risk areas. The findings highlight the combined impact of human and natural drivers on coastal pollution and offer valuable insights for environmental management, spatial planning, and policy interventions to mitigate future risks.Introduction Coastal areas require special attention due to the vulnerability of ecosystems and the dependence of human activities on them. Today, beaches have become one of the most popular destinations for tourists, and the presence of various coastal features creates diverse potentials for attracting visitors. Tourism, as a rapidly growing phenomenon, has become one of the largest industries in the world. The development of tourism and related recreational activities has a significant impact on the development patterns of regions and helps provide direct and indirect economic, social, cultural, and environmental benefits to host communities. However, at the same time, this development raises concerns and threats regarding environmental degradation and the destruction of the natural, historical, and cultural heritage of local residents. In fact, mass tourism does not equate to cleanliness or environmental sustainability. In this regard, some of the most significant impacts of tourism-related activities on the physical coastal environment include soil erosion, landslides, water quality degradation, shoreline alteration, and litter pollution. Litter pollution has become an undeniable threat to the sustainability of coastal ecosystems, and the threat posed by non-biodegradable plastic waste to coastal environments is increasingly evident. Pollution in the Caspian Sea poses a serious threat to aquatic life and human health. Unfortunately, coastal zones and estuaries are highly exposed to pollution, and this process endangers the survival of marine species. Urban environmental pollution in coastal areas can lead to a decline in water and soil quality, a reduction in biodiversity, health problems, and negative impacts on tourism and recreational activities.Material and MethodsThe main objective of this study is to identify and zone the coastal areas of the three counties of Anzali, Rezvanshahr, and Astara in terms of urban environmental pollution risk using the Analytic Hierarchy Process (AHP) and the Random Forest (RF) machine learning algorithm. In this regard, effective criteria were first identified. From an initial set of 24 criteria, 14 were selected: natural attractiveness, coastal degradation, river, road, human activity, urban drainage, population, urban waste, protection and management, facilities, security, natural landscapes, hotels, and the influence of tourism on population. Information layers for these were prepared in the ArcGIS Pro environment. Using the AHP method, the relative impact of each criterion was determined, and 70% of the high-risk areas were used as training data and 30% as test data for training the Random Forest algorithm. The predictive accuracy of the model results was evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC).Results and DiscussionThe results of validating the zoning maps using test data showed that the accuracy of the analytic hierarchy process and random forest models was 0.812 and 0.97, respectively. These results show that the random forest model has high accuracy in zoning coastal pollution. The results also show that the security criterion is the most significant in training the machine learning algorithm.The results of coastal zone pollution zoning in the analytic hierarchy process and random forest model showed that in Anzali County, 0.82% and 10.10% were in the very low-risk class, 0.55% and 16.55% in the low-risk class, 0.6% and 0.36% in the medium-risk class, 73.89% and 27.35% in the high-risk class, and 20.6% and 24.9% in the very high-risk class. In Rezvanshahr County, 0.57% and 12.55% were in the very low-risk class, 7.09% and 28.71% in the low-risk class, 21.27% and 22.68% in the medium-risk class, 66.64% and 98% in the high-risk class, and 0.45% and 0% in the very high-risk class. In Astara County, 0.02% and 26.73% were in the very low-risk class, 6.30% and 23.21% in the low-risk class, 23.87% and 31.20% in the medium-risk class, and 6.44% and 16.51% in the high-risk class.Conclusion Pollution from industrial and domestic wastewater entering the coastal waters is one of the major challenges on the northern coasts of Iran. In Bandar Anzali, this problem has been exacerbated due to the lack of adequate wastewater management infrastructure and poor enforcement of environmental laws. The general conclusion for the three cities of Astara, Bandar Anzali, and Rezvanshahr shows that all three regions face specific environmental pollution challenges caused by a combination of natural and human factors. Managers and planners can effectively reduce the risks of pollution in coastal areas by using the results of this research. Furthermore, these results will help facilitate the development and implementation of practical solutions.https://geoeh.um.ac.ir/article_46765_4b52ddb7a0b6ac8553536d004c895b59.pdfFerdowsi University of MashhadJournal of Geography and Environmental Hazards2322-168214320250923Landfill Suitability Site Selection Using Environmental Parameters and Exclusion Zones (Case Study: Dalahu County)Landfill Suitability Site Selection Using Environmental Parameters and Exclusion Zones (Case Study: Dalahu County)2352514709610.22067/geoeh.2025.93006.1564FASaied AsadollahHejaziAssociate Professor of Geomorphology University of Tabriz, Tabriz, Iran0000-0002-9289-5635ZahraZanganeh TabarPh.D. student in Geomorphology, University of Tabriz, Tabriz, Iran0009-0001-9007-8359Journal Article20250513One of the main contributors to environmental pollution and degradation is household and industrial waste. Therefore, the proper siting of landfill facilities plays a crucial role in controlling and reducing such pollution. Given the importance of this issue, the present study focuses on identifying suitable areas for landfill development in Dalaho County, Kermanshah Province. The research utilizes remote sensing techniques and multi-criteria decision-making (MCDM) methods. Key datasets used include MODIS, CHIRPS, Google Earth satellite imagery, 1:100,000-scale geological maps, and a 30-meter digital elevation model (DEM). The study employed various software tools, including ArcGIS, IDRISI, Super Decisions, and Google Earth Engine. Ten criteria were considered in the analysis: lithological features, distance from faults, elevation, slope, vegetation density, average annual rainfall, and distance from rivers, main roads, urban areas, and rural areas. The Analytic Network Process (ANP) was used for weighting the parameters, and the Weighted Linear Combination (WLC) method was applied to integrate the data layers. The results show that approximately 48% of Dalaho County has a high potential for pollution and environmental degradation, indicating that landfill sites should be located away from these areas. Conversely, around 31% of the area has low suitability for landfill development. Ultimately, only a small portion of the region about 8%, demonstrates moderate potential for landfill siting based on the evaluated criteria.IntroductionThe increasing trend of waste generation in recent years has emerged as a significant challenge for urban planners and environmentalists. Waste is one of the main contributors to environmental pollution and degradation, as well as to the spread of various diseases. Therefore, proper planning for waste management and disposal is critically important. One of the key measures in controlling pollution from waste is the appropriate siting of landfill facilities. Site selection based on environmental capabilities is a fundamental principle of urban and regional development planning. Identifying suitable areas for waste disposal plays a crucial role in mitigating environmental pollution and degradation. Different regions possess varying potentials for environmental planning due to their geomorphological, geological, and hydroclimatic conditions. Some areas, influenced by these factors, face significant constraints in accommodating activities such as landfill establishment. Dalaho County, located in Kermanshah Province within the folded Zagros structural zone, is one such region. Due to its geological and climatic characteristics, it contains substantial surface and groundwater resources. Moreover, tectonic activity, numerous faults and fractures, and permeable lithological formations make groundwater highly susceptible to contamination. This highlights the importance of accurately identifying suitable areas for landfill siting in the region. Accordingly, this study aims to determine potential landfill sites in Dalaho County.Material and MethodsThis study employed remote sensing methods to identify areas suitable for landfill siting in Dalaho County using ten key parameters: lithology, distance from faults, elevation, slope, vegetation density, average annual rainfall, distance from rivers, main roads, urban areas, and rural areas. To prepare lithology and fault maps, 1:100,000-scale digital geological maps from the National Geological Organization were used, while a 30-meter SRTM digital elevation model provided data on elevation, slope, and river networks. Google Earth Engine, along with MODIS and CHIRPS satellite imagery, was employed to generate maps of vegetation density and annual precipitation, and Google Earth images were used to identify urban areas and main roads. The analysis utilized several software tools, including ArcGIS (for fuzzification of data layers and map generation), Google Earth Engine (for vegetation and rainfall mapping), IDRISI (for implementing the Weighted Linear Combination, or WLC, model), and Super Decisions (for applying the Analytic Network Process, or ANP). The research was conducted in multiple stages: first, data layers were prepared and standardized (fuzzified); second, the layers were weighted using the ANP model; and finally, the integrated WLC-ANP model was applied to identify landfill-susceptible areas in Dalaho County.Discussion and ResultsCertain areas in the region possess high environmental and ecological value or are highly vulnerable to pollution, necessitating their exclusion from landfill siting. Therefore, based on environmental status, a literature review, and expert consultation, a restricted areas map was developed. This included buffer zones around rivers, urban areas, Azadi Dam Lake, faults, and regions with dense vegetation. The analysis showed that large portions of Dalaho County are unsuitable for waste disposal due to lithological, topographical, and hydrological constraints. In general, the eastern and southern parts of the county showed relatively higher potential for landfill development, whereas the central, western, and northern areas faced more significant limitations.ConclusionThe natural characteristics of Dalaho County render it highly vulnerable to environmental pollution, making landfill site selection particularly sensitive. This study applied an integrated WLC-ANP model to identify areas suitable for landfilling. Results indicate that approximately 48% of the county, comprising areas within 500 meters of rivers, 1 km from fault lines, 3 km from urban centers, 2 km from Azadi Dam Lake, and regions with dense vegetation, is highly susceptible to pollution and should be excluded from landfill development. About 31% of the area, primarily located in the western and northern regions, was classified as having low to very low suitability for landfill siting due to factors such as steep slopes, high elevations, fault proximity, and unsuitable lithology. Only around 8% of the county, located mainly in the eastern and southern regions, showed moderate potential for landfill establishment. Overall, the study emphasizes that Dalaho County’s natural setting, particularly its hydrogeomorphological features, must be carefully considered when planning waste disposal facilities. Proper site selection can play a vital role in preventing environmental degradation and protecting groundwater resources.One of the main contributors to environmental pollution and degradation is household and industrial waste. Therefore, the proper siting of landfill facilities plays a crucial role in controlling and reducing such pollution. Given the importance of this issue, the present study focuses on identifying suitable areas for landfill development in Dalaho County, Kermanshah Province. The research utilizes remote sensing techniques and multi-criteria decision-making (MCDM) methods. Key datasets used include MODIS, CHIRPS, Google Earth satellite imagery, 1:100,000-scale geological maps, and a 30-meter digital elevation model (DEM). The study employed various software tools, including ArcGIS, IDRISI, Super Decisions, and Google Earth Engine. Ten criteria were considered in the analysis: lithological features, distance from faults, elevation, slope, vegetation density, average annual rainfall, and distance from rivers, main roads, urban areas, and rural areas. The Analytic Network Process (ANP) was used for weighting the parameters, and the Weighted Linear Combination (WLC) method was applied to integrate the data layers. The results show that approximately 48% of Dalaho County has a high potential for pollution and environmental degradation, indicating that landfill sites should be located away from these areas. Conversely, around 31% of the area has low suitability for landfill development. Ultimately, only a small portion of the region about 8%, demonstrates moderate potential for landfill siting based on the evaluated criteria.IntroductionThe increasing trend of waste generation in recent years has emerged as a significant challenge for urban planners and environmentalists. Waste is one of the main contributors to environmental pollution and degradation, as well as to the spread of various diseases. Therefore, proper planning for waste management and disposal is critically important. One of the key measures in controlling pollution from waste is the appropriate siting of landfill facilities. Site selection based on environmental capabilities is a fundamental principle of urban and regional development planning. Identifying suitable areas for waste disposal plays a crucial role in mitigating environmental pollution and degradation. Different regions possess varying potentials for environmental planning due to their geomorphological, geological, and hydroclimatic conditions. Some areas, influenced by these factors, face significant constraints in accommodating activities such as landfill establishment. Dalaho County, located in Kermanshah Province within the folded Zagros structural zone, is one such region. Due to its geological and climatic characteristics, it contains substantial surface and groundwater resources. Moreover, tectonic activity, numerous faults and fractures, and permeable lithological formations make groundwater highly susceptible to contamination. This highlights the importance of accurately identifying suitable areas for landfill siting in the region. Accordingly, this study aims to determine potential landfill sites in Dalaho County.Material and MethodsThis study employed remote sensing methods to identify areas suitable for landfill siting in Dalaho County using ten key parameters: lithology, distance from faults, elevation, slope, vegetation density, average annual rainfall, distance from rivers, main roads, urban areas, and rural areas. To prepare lithology and fault maps, 1:100,000-scale digital geological maps from the National Geological Organization were used, while a 30-meter SRTM digital elevation model provided data on elevation, slope, and river networks. Google Earth Engine, along with MODIS and CHIRPS satellite imagery, was employed to generate maps of vegetation density and annual precipitation, and Google Earth images were used to identify urban areas and main roads. The analysis utilized several software tools, including ArcGIS (for fuzzification of data layers and map generation), Google Earth Engine (for vegetation and rainfall mapping), IDRISI (for implementing the Weighted Linear Combination, or WLC, model), and Super Decisions (for applying the Analytic Network Process, or ANP). The research was conducted in multiple stages: first, data layers were prepared and standardized (fuzzified); second, the layers were weighted using the ANP model; and finally, the integrated WLC-ANP model was applied to identify landfill-susceptible areas in Dalaho County.Discussion and ResultsCertain areas in the region possess high environmental and ecological value or are highly vulnerable to pollution, necessitating their exclusion from landfill siting. Therefore, based on environmental status, a literature review, and expert consultation, a restricted areas map was developed. This included buffer zones around rivers, urban areas, Azadi Dam Lake, faults, and regions with dense vegetation. The analysis showed that large portions of Dalaho County are unsuitable for waste disposal due to lithological, topographical, and hydrological constraints. In general, the eastern and southern parts of the county showed relatively higher potential for landfill development, whereas the central, western, and northern areas faced more significant limitations.ConclusionThe natural characteristics of Dalaho County render it highly vulnerable to environmental pollution, making landfill site selection particularly sensitive. This study applied an integrated WLC-ANP model to identify areas suitable for landfilling. Results indicate that approximately 48% of the county, comprising areas within 500 meters of rivers, 1 km from fault lines, 3 km from urban centers, 2 km from Azadi Dam Lake, and regions with dense vegetation, is highly susceptible to pollution and should be excluded from landfill development. About 31% of the area, primarily located in the western and northern regions, was classified as having low to very low suitability for landfill siting due to factors such as steep slopes, high elevations, fault proximity, and unsuitable lithology. Only around 8% of the county, located mainly in the eastern and southern regions, showed moderate potential for landfill establishment. Overall, the study emphasizes that Dalaho County’s natural setting, particularly its hydrogeomorphological features, must be carefully considered when planning waste disposal facilities. Proper site selection can play a vital role in preventing environmental degradation and protecting groundwater resources.https://geoeh.um.ac.ir/article_47096_71de73700199314daad7658522640fa5.pdf