جغرافیا و مخاطرات محیطی

جغرافیا و مخاطرات محیطی

بهبود ‌‌پیش‌بینی مکانی شوری خاک با مدل ترکیبی یادگیری ماشین در مناطق خشک شرق ایران

نوع مقاله : مقاله پژوهشی

نویسندگان
1 گروه مدیریت مناطق خشک و بیابانی، دانشکده منابع طبیعی و محیط زیست دانشگاه فردوسی مشهد، مشهد، ایران
2 گروه علوم خاک، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران
3 گروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه فردوسی مشهد، مشهد، ایران
چکیده
شوری خاک یکی از مخاطرات عمده ‌محیطی در مناطق خشک است که مستقیماً بر بهره‌وری منابع خاک و آب و پایداری اکوسیستم تأثیر می‌گذارد.  این مطالعه با هدف توسعه و بهینه‌سازی یک مدل یادگیری ماشین گروهی برای بهبود پیش‌بینی مکانی شوری خاک و شناسایی عوامل محیطی کلیدی کنترل‌کننده توزیع آن در دشت مه ولات، استان خراسان رضوی، ایران انجام شد. هدایت الکتریکی عصاره گل اشباع (ECe)   در ۱۳۷ نمونه خاک اندازه گیری شد. 32 متغیر کمکی، شامل؛ شاخص‌های طیفی سنجش از دور، توپوگرافی، زمین‌شناسی، کاربری اراضی و پارامترهای کیفیت آب‌های زیرزمینی، در فرآیند مدل‌سازی استفاده شد. چهار مدل یادگیری ماشین، شامل جنگل تصادفی  ( RF)، ماشین بردار پشتیبان (SVM )، درخت رگرسیون تقویت‌شده ( BRT) و مدل خطی تعمیم‌یافته ( GLM)، به همراه یک مدل ترکیبی، توسعه داده و ارزیابی شدند.  نتایج نشان داد که مدل ترکیبی نسبت به مدل‌های منفرد عملکرد بهتری داشته است (R² = 0.81, RMSE = 5.14, MAE = 4.11) به ویژه نسبت به مدل (SVM)   با (R² = 0.76)  . نقشه نهایی شوری نشان داد که مناطق بسیار شور عمدتاً در بخش‌های مرکزی، غربی و شمال غربی منطقه، به ویژه در اطراف پلایا بجستان، متمرکز بوده و افزایش شوری از شرق به غرب مشاهده شده است .پارامترهای کیفی آب زیرزمینی، به ویژه  HCO₃⁻، SAR  و TDS، بیشترین سهم را در ‌‌پیش‌بینی شوری خاک داشته‌اند. مدل ترکیبی همچنین توانایی بیشتری در تخمین مقادیر شدید شوری نشان داد (تا 21 دسی‌زیمنس بر متر). در مجموع، چارچوب پیشنهادی یک رویکرد دقیق و قابل اعتماد برای نقشه‌برداری شوری خاک و مدیریت پایدار منابع خاک ارائه می‌دهد.
کلیدواژه‌ها
موضوعات

©2026 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

 

 

Akbari, M., Modarres, R., & Alizadeh Noughani, M. (2020). Assessing early warning for desertification hazard based on E-SMART indicators in arid regions of northeastern Iran. Journal of Arid Environments, 174, 104086. https://doi.org/10.1016/j.jaridenv.2019.104086
Akbari, M., Ownegh, M., Asgari, H. R., Sadoddin, A., & Khosravi, H. (2016a). “Desertification Risk Assessment and Management Program.” Global Journal of Environmental Science and Management, 2 (4), 365–80. https://doi.org/10.22034/gjesm.2016.02.04.006
Akbari, M., Ownegh, M., Asgari, H. R., Sadoddin, A., & Khosravi, H. (2016b). Design and Development of Early Warning System for Desertification and Land Degradation. Environmental Resources Research, 4(2), 111-130. https://doi.org/10.22069/ijerr.2017.11207.1152
Akbari, M., Sarbazi, M., Sibevei, A., & Fadaie, S. (2024). Desertification Risk Assessment and Providing Management Strategies using the DPSIR-M Model in Khorasan Razavi province. Journal of Geography and Enviromental Hazards, 13(2), 210-239. [In Persian] https://doi.org/10.22067/geoeh.2023.83917.1404
Allbed, A., Kumar, L., & Aldakheel, Y. Y. (2014). Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region. Geoderma, 230–231, 1–8. https://doi.org/10.1016/j.geoderma.2014.03.025
Al-Refahi, M., Karimi, A., Mahmoudabadi, E., & Akbari, M. (2025). Evaluation of Soil Salinity Zonation Using Remote Sensing and Geostatistical Methods in the Alluvial Plain Lands of the Tigris River, Iraq. Journal of Geography and Environmental Hazards14(4), 85-104. [In Persian] https://doi.org/10.22067/geoeh.2025.93856.1579
Arnous, M. O., & Green, D. R. (2015). Monitoring and assessing waterlogged and salt-affected areas in the Eastern Nile Delta region, Egypt, using remotely sensed multi-temporal data and GIS. Journal of Coastal Conservation, 19, 369–391. https://doi.org/10.1007/s11852-015-0380-2
Bandak, S., Boali, A., Komaki, C. B., Ghorbani, K., & Alinejad, M. (2026). Machine learning-based estimation and spatial mapping of soil phosphorus and potassium using sentinel-2 and environmental covariates. Scientific Reportshttps://doi.org/10.1038/s41598-026-59967-w
Bandak, S., Boali, A., Yaghobi, S., & Taghizadeh-Mehrjardi, R. (2025). Ensemble machine learning approaches for estimating soil texture components in loess soils of Golestan Province. Earth Science Informatics18(2), 396. https://doi.org/10.1007/s12145-025-01897-8
Bannari, A., & Al-Ali, Z. M. (2020). Assessing climate change impact on soil salinity dynamics between 1987 and 2017 in arid landscapes using Landsat TM, ETM+, and OLI data. Remote Sensing, 12(17), 2794. https://doi.org/10.3390/rs12172794
Bargam, B., Boudhar, A., Kinnard, C., Bouamri, H., Nifa, K. & Chehbouni, A. (2024). Evaluation of the support vector regression (SVR) and the random forest (RF) models' accuracy for streamflow prediction under a data-scarce basin in Morocco. Discover Applied Sciences, 6, 306. https://doi.org/10.1007/s42452-024-05994-z
Basak, N., Rai, A. K., Barman, A., Mandal, S., Sundha, P., Bedwal, S., Kumar, S., Yadav, R. K., & Sharma, P. C. (2022). Salt-affected soils: Global perspectives. In Soil health and environmental sustainability: Application of geospatial technology (pp. 107–129). Springer. https://doi.org/10.1007/978-3-031-09270-1_6
Boali, A., & Akbari, M. (2026). Analysis of early warning signals of desertification hazard using time series of remote sensing–based indices (Case study: Mashhad County, Razavi Khorasan Province, Iran). Geography and Environmental Hazards, 15(2), 44–62. [In Persian] https://doi.org/10.22067/geoeh.2026.97882.1652
Boali, A., Asgari, H. R., Mohammadian Behbahani, A., Salmanmahiny, A., & Naimi, B. (2024a). Remotely sensed desertification modeling using an ensemble of machine learning algorithms. Remote Sensing Applications: Society and Environment, 34, 101149. https://doi.org/10.1016/j.rsase.2024.101149
Boali, A., Kariminejad, N., & Akbari, M. (2026). Spatial modeling of soil organic carbon using satellite imagery and machine learning for sustainable land management. In: Pourghasemi, H.R., Maleki, S. (Eds.), The Soil Nexus: Understanding Ecosystem Dynamics and Digital Innovations. Elsevier. https://doi.org/10.1016/B978-0-443-45286-4.00005-5
Boali, A., Kariminejad, N., & Hosseinalizadeh, M. (2024b). Enhancing wind erosion risk assessment through remote sensing techniques. PLoS ONE, 19(10), e0308854. https://doi.org/10.1371/journal.pone.0308854
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Brunner, P. H., Li, H. T., Kinzelbach, W., & Li, W. P. (2007). Generating soil electrical conductivity maps at the regional level by integrating measurements on the ground and remote sensing data. International Journal of Remote Sensing, 28(15), 3341–3361. https://doi.org/10.1080/01431160600928641
Dong, W., Wu, T., Luo, J., Sun, Y., & Xia, L. (2019). Land parcel-based digital soil mapping of soil nutrient properties in an alluvial-diluvial plain agricultural area in China. Geoderma, 340, 234–248. https://doi.org/10.1016/j.geoderma.2019.01.018
FAO. (2024). Global status of salt-affected soils: Main report. FAO. https://doi.org/10.4060/cd3044en
Gallant, J. C., & Dowling, T. I. (2003). A multiresolution index of valley bottom flatness for mapping depositional areas. Water Resources Research, 39(12), 1347. https://doi.org/10.1029/2002WR001426
Golestani, M., Ghahfarokhi, Z., Esfandiarpour, I., & Shirani, H. (2023). Evaluating the spatiotemporal variations of soil salinity in Sirjan Playa, Iran, using Sentinel-2A and Landsat-8 OLI imagery. Catena, 231, 107375. https://doi.org/10.1016/j.catena.2023.107375
Haq, Y. U., Shahbaz, M., Asif, S., Ouahada, K., & Hamam, H. (2023). Identification of soil types and salinity using MODIS Terra data and machine learning techniques in multiple regions of Pakistan. Sensors, 23(19), 8121. https://doi.org/10.3390/s23198121
Hassani, A., Azapagic, A., & Shokri, N. (2020). Predicting long-term dynamics of soil salinity and sodicity on a global scale. Proceedings of the National Academy of Sciences, 117(52), 33017–33027. https://doi.org/10.1073/pnas.2000925117
Hong-wei, W., Yong-hong, F., & Tiyip, T. (2011). The research of soil salinization human impact based on remote sensing classification in oasis irrigation area. Procedia Environmental Sciences, 10(2), 2399–2405. https://doi.org/10.1016/j.proenv.2011.09.373
Iwahashi, J., & Pike, R. J. (2007). Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology, 86(3–4), 409–440. https://doi.org/10.1016/j.geomorph.2006.09.012
John, K., Isong, I. A., Kebonye, N. M., Ayito, E. O., Agyeman, P. C., & Afu, S. M. (2020). Using machine learning algorithms to estimate soil organic carbon variability with environmental variables and soil nutrient indicators in an alluvial soil. Land, 9(12), 487. https://doi.org/10.3390/land9120487
Jordan, C. F. (1969). Derivation of leaf-area index from quality of light on the forest floor. Ecology, 50(4), 663–666. http://dx.doi.org/10.2307/1936256
Karimipour Reyhan, M., Momeni, H., & Shahsavar, A. (2024). Analyzing the regional relations of urban peripherals with the center of Zanjan province. Geography and Regional Future Studies, 1(3), 14-29. [In Persian] https://doi.org/10.30466/grfs.2024.54960.1026
Karimpour Reyhan, M., & Shahsavar, A. (2023). Land-use planning: A conceptual approach. Tahan Gostar Publishing. [In Persian]
Lämternicht, G. (2019). Spatial land-use planning: Capacity and feasibility for sustainable land-resource management (M. Karimpour Reyhan, S. Sharif Jahed, P. Amiri, & A. Shahsavar, Trans.; 2nd ed.). Tehran: University of Tehran Press [In Persian]
Li, W., Gao, S., Mu, X., Wen, Y., Javed, T., & Wang, Z. (2026). Exploring the driving forces of soil salinity reduction using random forest and SHAP in water-saving oasis irrigation areas. Irrigation Science, 44(1), 25. https://doi.org/10.1007/s00271-025-01045-6
Li, X., Li, Y., Wang, B., Sun, Y., Cui, G., & Liang, Z. (2022). Analysis of spatial-temporal variation of the saline-sodic soil in the west of Jilin Province from 1989 to 2019 and influencing factors. Catena, 217, 106492. https://doi.org/10.1016/j.catena.2022.106492
Liu, D., Zhang, Y., Zhang, J., Xiong, L., Liu, P., Chen, H., & Yin, J. (2021). Rainfall estimation using measurement report data from time-division long-term evolution networks. Journal of Hydrology, 600, 126530. https://doi.org/10.1016/j.jhydrol.2021.126530
Maleki, S., Fathizad, H., Karimi, A., Taghizadeh-Mehrjardi, R., & Pourghasemi, H. R. (2023). Monitoring of spatiotemporal changes of soil salinity and alkalinity in eastern and central parts of Iran. In Computers in Earth and Environmental Sciences (Chap. 40, pp. 547–561). Elsevier. https://doi.org/10.1016/B978-0-323-89861-4.00011-7
Maleki, S., Pouyan, S., Khoshoui, M. M., Tiefenbacher, J. P., & Pourghasemi, H. R. (2024). Detecting soil salinization, sodicity, and alkalization hazards within cultivated lands using digital soil mapping approaches. In Advanced tools for studying soil erosion processes (pp. 435–460). Elsevier. https://doi.org/10.1016/B978-0-323-90957-2.00018-X
Moore, I. D., Gessler, P. E., Nielsen, G. A. E., & Peterson, G. A. (1993). Soil attribute prediction using terrain analysis. Soil Science Society of America Journal, 57(2), 443–452. https://doi.org/10.2136/sssaj1993.03615995005700020026x
Mousavi, A., Karimi, A., Maleki, S., Safari, T., & Taghizadeh-Mehrjardi, R. (2023). Digital mapping of selected soil properties using machine learning and geostatistical techniques in Mashhad Plain, northeastern Iran. Environmental Earth Sciences, 82(9), 234. https://doi.org/10.1007/s12665-023-11050-z
Nabiollahi, K., Taghizadeh-Mehrjardi, R., Shahabi, A., Heung, B., Amirian-Chakan, A., Davari, M., & Scholten, T. (2021). Assessing agricultural salt-affected land using digital soil mapping and hybridized random forests. Geoderma, 385, 114858. https://doi.org/10.1016/j.geoderma.2020.114858
Nasrian, A., Akbari, M., Faridhosseini, A., & Neamatollahi, E. (2022). Spatio-temporal Monitoring of Groundwater Changes on Desertification Intensity in Agricultural Areas in Dargaz Plain, Khorasan Razavi Province. Desert Ecosystem Engineering, 7(21), 75-90. [In Persian] https://doi.org/10.22052/deej.2018.7.21.49
Page, A. L., Miller, R. H., & Keeney, D. R. (1982). Methods of soil analysis: Part 2. Chemical and microbiological properties (2nd ed.). ASA, SSSA, CSSA.
Rahimi, B., Afzali, M., Farhadi, F., & Alamolhoda, A. (2021). Reverse osmosis desalination for irrigation in a pistachio orchard. Desalination, 516, 115236. https://doi.org/10.1016/j.desal.2021.115236
Ray, S. S., Singh, J. P., Dutta, S., & Panigrahy, S. (2002). Analysis of within field variability of crop and soil using field data and spectral information as a pre-cursor to precision crop management.  The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 34(7), 302-307.
Richardson, A. D., Duigan, S. P., & Berlyn, G. P. (2002). An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytologist, 153(1), 185-194.
Rock, B. N., Vogelmann, J. E., Williams, D. L., Vogelmann, A. F., & Hoshizaki, T. (1985). Plant responses to stress may have spectral signatures that could be used to map, monitor, and measure forest damage. Remote Detection of Forest Damage, 36(7),439-445. https://doi.org/10.2307/1310339
Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95–107. https://doi.org/10.1016/0034-4257(95)00186-7
Rouse, J. W., Haas, R. H., Scheel, J. A., & Deering, D. W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings, 3rd Earth Resource Technology Satellite (ERTS) Symposium, 1, 48-62. https://ntrs.nasa.gov/citations/19740022614
Sharififar, A., & Sarmadian, F. (2023). Coping with the imbalanced data problem in digital mapping of soil classes. European Journal of Soil Science, 74(3), e13368. https://doi.org/10.1111/ejss.13368
Tarini, M., Cignoni, P., & Montani, C. (2006). Ambient occlusion and edge cueing to enhance real time molecular visualization. IEEE Transactions on Visualization and Computer Graphics, 12(5), 1237–1244. https://doi.org/10.1109/TVCG.2006.115⁠
Toomanian, N., Jalalian, A., Khademi, H., Karimian Eghbal, M., & Papritz, A. (2006). Pedodiversity and pedogenesis in Zayandeh-rud Valley, Central Iran. Geomorphology, 81(3), 376–393. https://doi.org/10.1016/j.geomorph.2006.04.016
Weiss, A. (2001). Topographic position and landforms analysis. In Proceedings of the ESRI International User Conference. San Diego, CA, USA
Xiao, C., Ji, Q., Chen, J., Zhang, F., Li, Y., Fan, J., Hou, X., Yan, F., & Wang, H. (2023). Prediction of soil salinity parameters using machine learning models in an arid region of northwest China. Computers and Electronics in Agriculture, 204, 107512. https://doi.org/10.1016/j.compag.2023.107512
Ye, L., Lin, X., Huang, L., Ji, K., Drost, T. A., Wan, W., & Zuo, J. (2026). Digital mapping of soil fauna abundance and diversity using Sentinel-1/2 and environmental attributes. Soil and Tillage Research, 255, 106796. https://doi.org/10.1016/j.still.2025.106796
Yin, W., Hu, Q., Liu, J., He, P., Zhu, D., & Boali, A. (2024). Assessing Climate and Land-Use Change Scenarios on Future Desertification in Northeast Iran: A Data Mining and Google Earth Engine-Based Approach. Land13(11), 1802. https://doi.org/10.3390/land13111802
Zinck, J. A. (1989). Physiography and soils. Lecture Notes for Soil Students, Soil Science Division, Soil Survey Courses. International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands.
ارسال نظر در مورد این مقاله
نام را وارد کنید.
نشانی پست الکترونیکی را به درستی وارد کنید.
وابستگی سازمانی را به درستی وارد کنید.
توضیحات را وارد کنید (حداقل 50 حرف)
CAPTCHA Image
شناسه امنیتی را به درستی وارد کنید.

مقالات آماده انتشار، پذیرفته شده
انتشار آنلاین از 01 شهریور 1405

  • تاریخ دریافت 09 خرداد 1405
  • تاریخ بازنگری 28 مرداد 1405
  • تاریخ پذیرش 30 مرداد 1405
  • تاریخ انتشار 01 شهریور 1405