- Ahmadpour, A., Mirhashemi, S., Haghighat jou, P., & Foroughi, F. (2022). Comparison of the monthly streamflow forecasting in Maroon dam using HEC-HMS and SARIMA models. Sustainable Water Resources Management, 8(5), 158. [In persian]
- Aliyari, M., Teshnehlab, M., & Khaki Sedigh, A. (2008). Short-term forecast of air pollution by neural networks, delayed memory line, gamma and ANFIS with PSO-based educational methods. Control journal, 2(1), 1-19. [In persian]
- Alshammari, R. K., Alrwais, O., & Aksoy, M. S. (2022). Machine learning applications to dust storms: a meta-analysis. Aerosol and Air Quality Research, 22(12), 220183.
- Al-Taei, A. I., Alesheikh, A. A., & Darvishi Boloorani, A. (2024). Hazardous Dust Source Susceptibility Mapping in Wet and Dry Periods of the Tigris-Euphrates Basin: A Meta-Heuristics and Machine Learning. Environmental Management Hazards, 10(4), 355-370.
- Ansari Ghojghar, M., Bazrafshan, J., Araghinejad, S., Parsi, E., & Soltani, S. (2020). Evaluation of the performance of the support-wavelet vector machine hybrid model in predicting dust storms (Case study: Sistan and Baluchestan province). Environmental Management Hazards, 7(4), 331-351. [In persian]
- Aryal, Y. (2022). Application of Artificial Intelligence Models for Aeolian Dust Prediction at Different Temporal Scales: A Case with Limited Climatic Data. AI, 3(3), 707-718. https://doi.org/10.3390/ai3030041.
- Asghari Sareskanrood, S., & Zeinali, B. (2014). Analyzing and Mapping of Dust Storms Seasonal Frequency over Iran for Hazards Reduction. Environmental Management Hazards, 1(2), 217-239. doi: 10.22059/jhsci.2014.53122. [In persian]
- Boloorani, A. D., Ranjbareslamloo, S., Mirzaie, S., Bahrami, H. A., Mirzapour, F., & Tehrani, N. A. (2020). Spectral behavior of Persian oak under compound stress of water deficit and dust storm. International journal of Applied earth Observation and Geoinformation, 88, 102082. [In persian]
- Bullard, J. E., Harrison, S. P., Baddock, M. C., Drake, N., Gill, T. E., McTainsh, G., & Sun, Y. (2011). Preferential dust sources: A geomorphological classification designed for use in global dust‐cycle models. Journal of Geophysical Research: Earth Surface, 116(F4).
- Dong, Z., Yu, X., Li, X., & Dai, J. (2013). Analysis of variation trends and causes of aerosol optical depth in Shaanxi Province using MODIS data. Chinese Science Bulletin, 58, 4486-4496.
- Falah Qalhar, G. & Sarvestan, R. (2020). Review and forecast of the phenomenon of dust in Khuzestan Province using Box-Jenkins time series model. Watershed Engineering and Management, 12(3), 608-620. doi: 10.22092/ijwmse.2018.115726.1363.
- Gholami, H., Mohamadifar, A., Sorooshian, A., & Jansen, J. D. (2020). Machine-learning algorithms for predicting land susceptibility to dust emissions: The case of the Jazmurian Basin, Iran. Atmospheric pollution research, 11(8), 1303-1315. [In persian]
- Hallaj, Z., Sediqi, H., & Farhadian, H. (2015). Environmental effects of dust storms in southeast Iran (case study: Hamoon Lagoon). In International Conference on New Researches in Agricultural and Environmental Sciences, Tehran, Economy and Energy Association. [In Persian].
- jahanbakhshasl S, mohammadkhorshiddoust A, abbsighasrik F, abbasighasrik Z. (2024). Precipitation, Time Series Models, Man-Kendall, Holt Winters model, West Azerbaijan Province. jgs. 24(75), 98-115. doi:61186/jgs.24.75.10. [In persian]
- Jish Prakash, P., Stenchikov, G., Kalenderski, S., Osipov, S., & Bangalath, H. (2015). The impact of dust storms on the Arabian Peninsula and the Red Sea. Atmospheric Chemistry and Physics, 15(1), 199-222.
- Khaniabadi, Y. O., Daryanoosh, S. M., Amrane, A., Polosa, R., Hopke, P. K., Goudarzi, G., ... & Armin, H. (2017). Impact of Middle Eastern Dust storms on human health. Atmospheric pollution research, 8(4), 606-613. [In persian]
- Liu, Y., Wang, G., Hu, Z., Shi, P., Lyu, Y., Zhang, G., ... & Liu, L. (2020). Dust storm susceptibility on different land surface types in arid and semiarid regions of northern China. Atmospheric research, 243, 105031.
- Melody Farahbakhsh , Bohlool Alijani, Ebrahim Fattahi, (2015). Synoptic analysis of Iran dust storm hazard (July 30 to August 2, 2012), Journal of Environmental Management Hazard, 2(1), 5-20. magiran.com/p2119723. [In persian]
- Moody, J., & Darken, C. J. (1989). Fast learning in networks of locally-tuned processing units. Neural computation, 1(2), 281-294.
- Naghibi, A., Hashemi, H., Zhao, P., Brogaard, S., Eklund, L., Hassan, H. H., & Mansourian, A. (2024). Spatiotemporal variability of dust storm source susceptibility during wet and dry periods: The Tigris-Euphrates River Basin. Atmospheric pollution research, 15(1), 101953.
- Namdari, S., Karimi, N., Sorooshian, A., Mohammadi, G., & Sehatkashani, S. (2018). Impacts of climate and synoptic fluctuations on dust storm activity over the Middle East. Atmospheric environment, 173, 265-276. [In persian]
- Neelamani, S., & Al-Dousari, A. (2016). A study on the annual fallout of the dust and the associated elements into the Kuwait Bay, Kuwait. Arabian Journal of Geosciences, 9, 1-11.
- O’Loingsigh, T., McTainsh, G. H., Tews, E. K., Strong, C. L., Leys, J. F., Shinkfield, P., & Tapper, N. J. (2014). The Dust Storm Index (DSI): a method for monitoring broadscale wind erosion using meteorological records. Aeolian Research, 12, 29-40.
- Pan, H., Gui, G., Lin, Z., & Yan, C. (2018). Deep BBN learning for health assessment toward decision-making on structures under uncertainties. KSCE Journal of Civil Engineering, 22, 928-940.
- 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 Quarterly, 52(4), 567-587. [In persian]
- Raeispour, K. (2008). Statistical Analysis and Isometropia of Dust in Khuzestan Province. MS. c dissertation, Sistan and Baluchestan University, 189. [In persian]
- Rashki, A., Kaskaoutis, D. G., Eriksson, P. G., de W. Rautenbach, C. J., Flamant, C., & Abdi Vishkaee, F. (2014). Spatio-temporal variability of dust aerosols over the Sistan region in Iran based on satellite observations. Natural hazards, 71, 563-585. [In persian]
- Rezazadeh, M., Irannejad, P., & Shao, Y. (2013). Climatology of the Middle East dust events. Aeolian Research, 10, 103-109. [In persian]
- Rivas, V. M., Merelo, J. J., Castillo, P. A., Arenas, M. G., & Castellano, J. G. (2004). Evolving RBF neural networks for time-series forecasting with EvRBF. Information Sciences, 165(3-4), 207-220.
- Samadi, M., Darvishi Boloorani, A., Alavipanah, S. K., Mohamadi, H., & Najafi, M. S. (2014). Global dust Detection Index (GDDI); a new remotely sensed methodology for dust storms detection. Journal of environmental health science and engineering, 12, 1-14. [In persian]
- Satapathy, S. K., Dehuri, S., Jagadev, A. K., & Mishra, S. (2019). EEG Signal Classification Using RBF Neural Network Trained With Improved PSO Algorithm for Epilepsy Identification. EEG Brain Signal Classification for Epileptic Seizure Disorder Detection; Satapathy, SK, Dehuri, S., Jagadev, AK, Mishra, S., Eds, 67-89.
- Shepherd, G., Terradellas, E., Baklanov, A., Kang, U., Sprigg, W., Nickovic, S., ... & Joowan, C. (2016). Global assessment of sand and dust storms.
- Sun, J. H., Zhao, L. N., & Zhao, S. X. (2003). An integrated modeling system of dust storm suitable to north China and applications. Clim Environ Res, 8, 125-142.
- Ukhov, A., Mostamandi, S., Da Silva, A., Flemming, J., Alshehri, Y., Shevchenko, I., & Stenchikov, G. (2020). Assessment of natural and anthropogenic aerosol air pollution in the Middle East using MERRA-2, CAMS data assimilation products, and high-resolution WRF-Chem model simulations. Atmospheric Chemistry and Physics Discussions, 2020, 1-42.
- UNEP, WMO, UNCCD, 2016. Global Assessment of Sand and Dust Storms. United Nations Environment Programme, Nairobi.
- Wan, A., Chang, Q., Khalil, A. B., & He, J. (2023). Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism. Energy, 282, 128274.
- (1995). Manual on Codes – International Codes. WMO Report No.306, Geneva, Switzerland
- World Bank. (2019). Sand and Dust Storms in the Middle East and North Africa Region: Sources, Costs, and Solutions.
|