Abdolshahnejad, M., Khosravi, H., Nazari Samani, A., Zehtabian, G., & Alambaigi, A. (2020). Determining the Conceptual Framework of Dust Risk Based on Evaluating Resilience (Case Study: Southwest of Iran). Strategic Research Journal of Agricultural Sciences and Natural Resources, 5(1), 33-44. [In Persian] https://doi.org/10.22047/srjasnr.2020.113050
Aboagye-Sarfo, P., Mai, Q., Sanfilippo, F. M., Preen, D. B., Stewart, L. M., & Fatovich, D. M. (2015). A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia. Journal of Biomedical Informatics, 57, 62-73. https://doi.org/10.1016/j.jbi.2015.06.022
Ahmadpour, A., Mirhashemi, S. H., & Panahi, M. (2021). Evaluation of neural network algorithms, and time-series models and SARIMA-SETAR hybrid model in Monthly wind speed prediction. Journal of Arid Biome, 10(2), 131-146. [In Persian] https://doi.org/10.29252/aridbiom.2021.15523.1828
Ahmed, N. K., Atiya, A. F., Gayar, N. E., & El-Shishiny, H. (2010). An empirical comparison of machine learning models for time series forecasting. Econometric Reviews, 29(5-6), 594-621.
Ansari Ghojghar, M., & Pourmohammad, P. (2025). Implementation of the Tri-Hybrid RBF-GA-SARIMA Meta-Model for Dust Storm Modeling (Case Study: Sistan and Baluchestan Province). Environmental Management Hazards, 11(4), 305-322. [In Persian] https://doi.org/10.22059/jhsci.2025.387276.859
Ansari Ghojghar, M., Araghinejad, S., Bazrafshan, J., Zahraie, B., & Parsi, E. (2021). Evaluating the Performance of GRU-LSTM Hybrid Model in Predicting the Dust Storms Events (Case Study: Khuzestan Province in Southwest of Iran). Iran-Water Resources Research, 17(1), 16-32. [In Persian] https://dor.isc.ac/dor/20.1001.1.17352347.1400.17.1.2.3
Ansari Ghojghar, M., Bazrafshan, J., & Araghinejad, S. (2022). Evaluating the Efficiency of Hybrid Metamodels of Machine Learning and Box Jenkins in Order to Model Dust Storms (Case Study: Khuzestan Province). Iranian Journal of Soil and Water Research, 53(8), 1695-1714. [In Persian] https://doi.org/10.22059/ijswr.2022.346694.669335
Ansari Ghojghar, M., Pourgholam-Amiji, M., Bazrafshan, J., Liaghat, A., & Araghinejad, S. (2020). Performance Comparison of Statistical, Fuzzy and Perceptron Neural Network Models in Forecasting Dust Storms in Critical Regions in Iran. Iranian Journal of Soil and Water Research, 51(8), 2051-2063. [In Persian] https://doi.org/10.22059/ijswr.2020.302529.668607
Arjomandi, H., Kheiralipour, K., & Amarloei, A. (2024). Prediction of dust concentration on a laboratory scale using image processing and artificial intelligence technologies. Journal of Research in Mechanics of Agricultural Machinery, 13(2), 1-9. [In Persian] https://doi.org/10.22034/jrmam.2024.14177.642
Azizi, G., Shamsipour, A., Miri, M., & Safarrad, T. (2012). Statistic and Synoptic Analysis of Dust Phenomena in West of Iran. Journal of Environmental Studies, 38(3), 123-134. [In Persian] https://doi.org/10.22059/jes.2012.29154
Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time series Analysis: Forecasting and Control. 3rdEd, prentice Hall, Englewood Cliffs Inc, New Jersey, 598.
Dargahian, F., Yasrebi, B., & Khosrowshahi, M. (2022). Study the Dependence Between Climatic Factors and Dust Rise from Inrenal Hotspots in Khouzestan. Extension and Development of Watershed Management, 9(35), 11-19. [In Persian] https://www.wmji.ir/article_254170.html
Draxler, R. R. D., & Rolph, G. D. (2003). HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) Model access via NOAA ARL READY Website. http://www.arl.noaa.gov/ready/ hysplit4.html
Draxler, R., & Hes G. D. (1998). An overview of the HYSPLIT_4 modeling system for trajectories, dispersion and Deposition. Australian Meteorological Magazine, 47, 295-308.
Draxler, R., Stunder, B., Rolph, G., Stein, A., & Taylor, A. (2009). Hybrid single-particle Lagrangian integrated trajectories 4 user's guide. NOAA Tech. aMemo, ERL-ARL.
Du, P., Huang, Z., Tang, S., Dong, Q., Bi, J., Yu, X., & Gu, Q. (2023). Long-term Variation of Dust Devils in East Asia during 1959-2021. Authorea Preprints, 128(9), e2022JD038013. https://doi.org/10.1029/2022JD038013
Ebrahimi-Khusfi, Z., Taghizadeh-Mehrjardi, R., & Nafarzadegan, A. R. (2021). Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions. Environmental Science and Pollution Research, 28, 6796–6810. https://doi.org/10.1007/s11356-020-10957-z
Farzanehpey, F., Ranjbar-Fordoe, A., Khosravi, H., & Mosavi, S. H. (2024). Evaluation of dust changes and its relationship with temperature (Case study: Khuzestan province). Integrated Watershed Management, 4(1), 16-29. [In Persian] https://doi.org/10.22034/iwm.2024.2014553.1112
Fattahi Masrour, P., & Rezazadeh, M. (2022). Spatio-Temporal Distribution of Various Types of Dust Events in the Middle East during the Period 1996-2015. Journal of the Earth and Space Physics, 47(4), 231-248. https://doi.org/10.22059/jesphys.2021.321010.1007306
Fotouhi Firoozabad, F., & Malekinejad, H. (2020). Analysis and Zonation of Maximum 24-Hour Rainfall of Iran Using Wakeby Distribution and Geostatistic Technique. Desert Management, 7(14), 75-92. [In Persian] https://doi.org/10.22034/jdmal.2020.38477
Hosseini, A., Alijani, B., & Waghei, Y. (2020). Predicting the Annual Dusty Days in Khorasan Razavi Province Using Spatial-Temporal Analysis. Journal of Geography and Environmental Hazards, 8(4), 103-117. [In Persian] https://doi.org/10.22067/geo.v0i0.79116
Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of Statistical Software, 27(3), 1-22. https://doi.org/10.18637/jss.v027.i03
Jahanbakhshasl, S., Khorshiddoust, A. M., abbsighasrik, F., & abbasighasrik, Z. (2024). Precipitation, Time Series Models, Man-Kendall, Health Winters model, West Azerbaijan Province. Journal of Applied Researches in Geographical Sciences, 24(75), 98-115. [In Persian] http://dx.doi.org/10.61186/jgs.24.75.10
Karegar, M E., Bodagh Jamali, J., Ranjbar Saadat Abadi, A., Moeenoddini, M., & Goshtasb, H. (2017). Simulation and Numerical Analysis of severe dust storms Iran East. Journal of Spatial Analysis Environmental Hazards, 3(4), 101-119. [In Persian] http://jsaeh.khu.ac.ir/article-1-2658-fa.html
Keykhosravi, S. S., Nejadkoorki, F., & Amintoosi, M. (2019). Estimation of Artificial Neural Networks (MLP and RBF) Accuracy in Anticipation of the Dust of the Sabzevar Cement Factory. Journal of Research in Environmental Health, 5(1), 43-52. [In Persian] https://doi.org/10.22038/jreh.2019.38083.1277
Khosroshahi, M., Saeedifar, Z., Shahbazi, K., Zandifar, S., Lotfinasabasl, S., Gohardoust, A., … & Khodagholi, M. (2024). Investigating the trend of temporal and spatial changes of dusty days and determining the contribution of climatic elements on its spread in Iran. Iranian Journal of Range and Desert Research, 30(4), 521-541. [In Persian] https://doi.org/10.22092/ijrdr.2024.130966
Kim, J. (2019). Particulate Matter Mortality Rates and Their Modification by Spatial Synoptic Classification. International Journal of Environmental Research and Public Health, 16(11), 1904. https://doi.org/10.3390/ijerph16111904
Mahmoudi, L., & Ikegaya, N. (2023). Identifying the Distribution and Frequency of Dust Storms in Iran Based on Long-Term Observations from over 400 Weather Stations. Sustainability, 15(16), 12294. https://doi.org/10.3390/su151612294
Mahmoudi, L., Amiri Doumari, S., Safarianzengir, V., Maleki, R., Kianinejad, S., & Kianian, M. K. (2020). Monitoring and Prediction of Dust and Investigating its Environmental Impacts in the Western Half of Iran and the Eastern Borders of Turkey and Iraq, Using Remote Sensing and GIS. Journal of the Indian Society of Remote Sensing, 49(5), 713-724. https://doi.org/10.1007/s12524-020-01224-2
Mirzadeh, S. M., Nejadkoorki, F., Moosavi, V., & Mirhoseini, S. A. (2021). Comparison of the accuracy of the support vector regression model with two common methods of artificial neural network and adaptive neuro-fuzzy inference system in predicting the pollutant concentration of the PM10. Journal of Natural Environment, 74(1), 167-179. [In Persian] https://doi.org/10.22059/jne.2021.307289.2041
Mohammadpour Penchah, M., Memarian, M. H., & Mirrokni, S. M. (2015). Modeling and Analysis of Dust Storms of Yazd Province Using Numerical Models. Journal of Geography and Environmental Hazards, 3(4), 67-83. [In Persian] https://doi.org/10.22067/geo.v3i4.34323
Mourianizadeh, S., Khoorani, A., & Sharif, M. (2024). Modeling dust storm based on spectral dust indicators and artificial intelligence in Hormozgan province. Iranian Journal of Geophysics, 18(4), 39-57. [In Persian] https://doi.org/10.30499/ijg.2024.418114.1543
Movahedi, S., Heydari Naserabad, B., Hashemi Ana, S. K., & Ranjbar, B. (2012). Zoning of climatic regions of Khuzestan province. Quarterly Journal of Geographical Space Research, 12(40), 73-64. [In Persian]
Nabavi, S. S., Moradi, H., & Shrifikia, M. (2019). Evaluation of dust storm temporal distribution and the relation of the effective factors with the frequency of occurrence in Khuzestan Province from 2000 to 2015. Scientific- Research Quarterly of Geographical Data (SEPEHR), 28(111), 191-203. [In Persian] https://doi.org/10.22131/sepehr.2019.37518
Negahban, S., Ganjaeian, H., Ghisarian, S. S., & Ebrahimi, A. (2024). Identifying the centers of dust and analyzing the factors influencing its occurrence Based on Remote Sensing Data (Case Study: Southwest Iran). Journal of Geography and Environmental Hazards, 13(4), 386-405. [In Persian] https://doi.org/0.22067/geoeh.2024.89088.1504
Nejadkoorki, F., & Baroutian, S. (2012). Forecasting Extreme PM10 Concentrations Using Artificial Neural Networks. International Journal of Environmental Research, 6(1), 277-284. [In Persian] https://doi.org/10.22059/ijer.2011.493
Pourgholam Amiji, M., Ansari Ghojghar, M., Bazrafshan, J., Liaghat, A., & Araghinejad, S. (2020). Comparing the Performance of SARIMA and Holt-Winters Time Series Models With Artificial Intelligence Methods in Dust Storms Forecasting (Case Study: Sistan and Baluchestan Province). Physical Geography Research, 52(4), 567-587. [In Persian] https://doi.org/10.22059/jphgr.2021.303847.1007524
Pourgholam-Amiji, M., Ansari Ghojghar, M., & Ahmadaali, K. (2021). Prediction of Dust Storms in Khuzestan Province Using Artificial Neural Networks. Nivar, 45(114-115), 56-75. [In Persian] https://doi.org/10.30467/nivar.2021.303747.1200
Pourmaafi Esfahani, E., Almodaresi, A., Mousaei Sanjerehei, M., & Hghparast, H. (2023). Evaluation of dust emission using artificial neural network model of Kashan city. Environmental Sciences, 21(2), 69-80. [In Persian] https://doi.org/10.48308/envs.2023.1177
Rahi, G. R., Bahraini, F., KhosroShahi, M., & Biabani, L. (2022). The Effect of Drought on Dust Storm Frequency (Case study: Bushehr Province). Journal of Water and Soil Conservation, 29(1), 31-51. [In Persian] https://doi.org/10.22069/jwsc.2022.19677.3511
Safarian Zengir, V., Zenali, B., Jafari Hasi Kennedy, Y., & Jafarzadeh, L. (2018). Investigation of dust and evaluation of its prediction in Ardebil province using ANFIS model. Journal of Spatial Analysis Environmental Hazards, 5(2), 107-124. [In Persian] http://jsaeh.khu.ac.ir/article-1-2771-fa.html
Saligheh, M., & Kakhaki Mehneh, H. (2015). Investigating the Relationship among Climate Factors and Air Pollution Fluctuation (Case Study of the City of Mashhad). Journal of Geography and Environmental Hazards, 4(2), 77-94. [In Persian] https://doi.org/10.22067/geo.v4i2.31769
Shaiba, H., Alaashoub, N. S., & Alzahrani, A. A. (2018). Applying machine learning methods for predicting sand storms. Paper presented at the Proceedings of the 1st International Conference on Computer Applications & Information Security (ICCAIS). Riyadh, Saudi Arabia. http://dx.doi.org/10.1109/CAIS.2018.8441998
Sobhani, B., & Safarian Zengir, V. (2019). Analysis and prediction of Dust phenomenon in the southwest of Iran. Journal of Natural Environmental Hazards, 8(22), 179-198. [In Persian] https://doi.org/10.22111/jneh.2019.28148.1481
Taghavi, F., Owlad, E., Safarrad, T., & Irannejad, P. (2013). Identifying and monitoring dust storm in the western part of Iran using remote sensing techniques. Journal of the Earth and Space Physics, 39(3), 83-96. [In Persian] https://doi.org/10.22059/jesphys.2013.35600
Xiao, F., Wong, M. S., Lee, K. H., Campbell, J. R., & Shea, Y. K. (2015). Retrieval of dust storm aerosols using an integrated neural network model. Computers & Geosciences, 85, 104-114. http://dx.doi.org/10.1016/j.cageo.2015.02.016
Zhu, Y. M., Lu, X. X., & Zhou, Y. (2007). Suspended sediment flux modeling with artificial neural network: An example of the longchuanjiang river in the upper yangtze catchment. Geomorphology, 84(4), 111-125. https://doi.org/10.1016/j.geomorph.2006.07.010
ارسال نظر در مورد این مقاله