Abbasi, H., Jalili, A., Kosroshahi, M., Fayaz, M., Khaksarian, F., Kenshlo, H., ... & Opp, C. (2024). Land management to control sand and dust storm sources–case study: The dust hotspot of south-eastern Ahvaz, Iran. In E3S Web of Conferences (Vol. 575, p. 07003). EDP Sciences.
Adebiyi, A., Kok, J. F., Murray, B. J., Ryder, C. L., Stuut, J. B. W., Kahn, R. A., ... & Meng, J. (2023). A review of coarse mineral dust in the Earth system, Aeolian Res., 60, 100849.
Aghelpour, P., Singh, V. P., & Varshavian, V. (2021). Time series prediction of seasonal precipitation in Iran, using data-driven models: a comparison under different climatic conditions. Arabian Journal of Geosciences, 14, 1-14. (In Persian)
Ahmadpour, A., Mirhashemi, & Panahi. (2021). Evaluation of neural network algorithms, time series models, and the SARIMA-SETAR hybrid model in monthly wind speed prediction. Arid Biome, 10(2), 131–146. (In Persian)
Ansari Ghojghar, M. A., Pourgholam-Amiji, M., Bazrafshan, J., Araghinejad, S., Liaghat, A., & Hosseini-Moghari, S. M. (2021). Performance Evaluation of Genetic Algorithm and GA-SA Hybrid Method in Forecasting Dust Storms (Case Study: Khuzestan Province). Soil and Water Research, 51(10), 2623-2639. (In Persian)
Ansari Ghojghar, M., Araqi Nejad, Sh., Bazrafshan, J., Zahraei, B., & Parsi, E. (2021). Assessing the efficiency of the GRU-LSTM hybrid model in predicting dust storms (Case study: Khuzestan province). (In Persian)
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)
Ansari Ghojghar, M., Pourgholam-Amiji, M., Araghinejad, S., Zahraie, B., Razavi, S., & Salajegheh, A. (2021). Evaluation of the Combination of ANFIS Model with Metaheuristic Optimization Algorithms in Predicting Dust Storms of Khuzestan Province. Journal of Range and Watershed Managment, 73(4), 691-708. (In Persian)
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)
Araghinejad, S., Ansari Ghojghar, M., PourGholam Amigi, M., Liaghat, A., & Bazrafshan, J. (2022). The effect of climate fluctuation on frequency of dust storms in Iran. Desert Ecosystem Engineering, 7(21), 13-32. (In Persian)
Arami, S. H., Karimi Sangchini, E., Alimahmoodi Sarab, S., Dinarvand, M. and Yasrebi, B. (2024). Mapping spatial and temporal pattern of risk and hazard of dust storms in Khuzestan province. Journal of Arid Biome, 14(1), 61-78.
Awadh, S. M. (2023). Impact of North African sand and dust storms on the Middle East using Iraq as an example: Causes, sources, and mitigation. Atmosphere, 14(1), 180.
Bahrami, H.-A., Jalali, J., & Darvishi Balourani, B. (2014). Spatiotemporal modeling of dust storm occurrences in Khuzestan province. Iranian Journal of Remote Sensing and GIS, 5(2). (In Persian)
Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
de Jesús Guevara-Macías, M., Pineda-Martínez, L. F., & Carbajal, N. (2023). A descriptive study of dust storms and air quality in a semi-arid region of Mexico. Air Quality, Atmosphere & Health, 16(8), 1645-1660.
Dorigo, M. (2007). Ant colony optimization. Scholarpedia, 2(3), 1461.
Fallah Ghalehri, F., & Sarvestan, S. (2020). Analysis and forecasting of dust phenomena in Khuzestan province using Box-Jenkins time series models. Watershed Engineering and Management Journal, 12(3), 608–620. (In Persian)
Gillette, D. A. (1981). Production of dust that may be carried great distances.
Golestan, A., & Molai-Zahedi, Z. (2013). A review of swarm intelligence-based optimization algorithms. Conference on Computer. (In Persian)
Gorissen, B. L., Yanıkoğlu, İ., & Den Hertog, D. (2015). A practical guide to robust optimization. Omega, 53, 124-137.
Goudie, A. S., & Middleton, N. J. (2006). Desert dust in the global system. Springer Science & Business Media.
Guo, Y. T., Zhang, J., Wang, S. G., She, F., & Li, X. (2011). Long-term characterization of major water-soluble inorganic ions in PM 10 in coastal site on the Japan Sea. Journal of atmospheric chemistry, 68, 299-316.
Hahnenberger, M., & Nicoll, K. (2014). Geomorphic and land cover identification of dust sources in the eastern Great Basin of Utah, USA. Geomorphology, 204, 657-672.
Helmi Mahtab, M., Bakhtiari Bahram, B., & Qadri Kourosh, K. (2020). Modeling and predicting meteorological drought using the SARIMA time series model in various climatic regions of Iran. (in persian)
Jahanbakhsh-Asl, M., Khorshiddoost, M., Abbasi-Qasrekik, & Abbasi-Qasrekik. (2024). Comparative application of time series models in predicting precipitation in West Azerbaijan province. Applied Research in Geographical Sciences, 24(75), 98–115. (In Persian)
Karahan, H., Gurarslan, G., & Geem, Z. W. (2013). Parameter estimation of the nonlinear Muskingum flood-routing model using a hybrid harmony search algorithm. Journal of Hydrologic Engineering, 18(3), 352-360.
Kargar, E., Badaq Jamali, J., Ranjbar Sa'adat Abadi, A., Moeinoddini, M., & Gashtasb, H. (2016). Simulation and numerical analysis of severe dust storms in eastern Iran. Spatial Analysis of Environmental Hazards, 3(4), 101–119. (In Persian)
Li, A., & Draine, B. T. (2002). Infrared Emission from Interstellar Dust. III. The Small Magellanic Cloud. The Astrophysical Journal, 576(2), 762.
Middleton, N., Kashani, S. S., Attarchi, S., Rahnama, M., & Mosalman, S. T. (2021). Synoptic causes and socio-economic consequences of a severe dust storm in the Middle East. Atmosphere, 12(11), 1435.
Naseri, S. H., & Chitgar, S. (2020). A new method for solving multi-objective stochastic optimization models with chance constraints. Operational Research and Its Applications (Applied Mathematics), 17(3), 63–79. (In Persian)
Nickling, W. G., & Gillies, J. A. (1989). Emission of fine-grained particulates from desert soils. In Paleoclimatology and Paleometeorology: modern and past patterns of global atmospheric transport (pp. 133-165). Dordrecht: Springer Netherlands.
Panicker, N. K. K., & Valarmathi, J. (2024). Time series prediction of aerosol optical depth across the northern Indian region: integrating PSO-optimized SARIMA-SVR based on MODIS data. Acta Geophysica, 1-30.
Pourgholam Amiji, A., Ansari Qojaqar, M., Bazrafshan, J., Liaqaat, A., & Araqi Nejad, Sh. (2020). Comparing the performance of SARIMA and Holt-Winters time series models with artificial intelligence methods in forecasting dust storms (Case study: Sistan and Baluchestan province). Journal of Natural Geography Research, 52(4), 567–587. (In Persian)
Shafieenejad, I., Dehkordi, M. R. B., & Amin, M. (2024). A review of the application of optimization algorithms nature inspired in the design of flight paths. Technology in Aerospace Engineering, 8(3), 75-99. (In Persian)
Shuker, Z. (2022). Dust Storms and Climate Change: A Crisis for the Iraqi Economy, and the Need for Multilateral Solutions. The Institute of Regional and International Studies: Islamabad, Iraq.
Sobhani, B., Safarian Zengir, V., & Faizollahzadeh, S. (2020). Modeling and prediction of dust in western Iran. Physical Geography Research Quarterly, 52(1), 17-35. (In Persian)
Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European journal of operational research, 185(3), 1155-1173.
Sun, Y., Zhuang, G., Yuan, H., Zhang, X., & Guo, J. (2004). Characteristics and sources of 2002 super dust storm in Beijing. Chinese Science Bulletin, 49, 698-705.
UNEP, W. (2021). UNCCD (2016) Global assessment of sand and dust storms. United Nations Environment Programme, Nairobi.
Zeynali, M. J., & Bilandi, M. P. (2018). Estimation of optimal parameters of the nonlinear Muskingum routing model using the continuous ant colony algorithm. Iranian Journal of Irrigation and Drainage Engineering, 8(3), 94–106. (in persian)
Zhu, W., & Duan, H. (2014). Chaotic predator–prey biogeography-based optimization approach for UCAV path planning. Aerospace science and technology, 32(1), 153-161.
)