Performance Comparison of Statistical, Fuzzy and Perceptron Neural Network Models in Forecasting Dust Storms in Critical Regions in Iran

Document Type : Research Paper


1 Phd Candidate of Water Resources Engineering, Department of Irrigation and Reclamation Engineering, University of Tehran, Karaj, Iran.

2 Department of Irrigation and Reclamation Engineering, Faculty of Agriculture Engineering & Technology, Campus of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

3 Department of Irrigation & Reclamation Engineering, Campus of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.


Different regions have different potentials in dust release, and the increase in dust storms indicates the dominance of the desert ecosystem in each region. Prediction of the occurrence of dust storms in critical regions allow desion-makers to efficiently manage and to mitigate its probable damages to landscape. This study aims to predict the frequency of dust storm days (FDSD) over two critical regions (west and southeast) in Iran on a seasonal  scale. To this end, the hourly dust data and World Meteorological Organization codes were gathered in six synoptic stations of Zabol and Zahedan (southeast Iran), Abadan, Ahvaz, Bostan, and Masjed Soleiman (west Iran) covering the statistical period of 25 years (1990-2014). After calculating the frequency of dust storm days, using four artificial intelligence methods including multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), radial basis function (RBF), and general regression neural network (GRNN), the frequency of dust storm days for the next season were predicted. The results showed an increase in the accuracy of the predictions with increasing the number of dust storm days in such a way that based on the results obtained from the MLP model, the correlation coefficient between the observed and predicted values ​​of the frequency of dust storm days for Masjed Soleiman and Zabol stations were 0.8 and 0.97, respectively; explaining that Zabol have the highest frequency among these stations. Also, according to the results of ANFIS, RBF, and GRNN models, the correlation coefficient calculated for prediction in Masjed Soleiman and Zabol stations varied from 0.41 to 0.95, 0.35 to 0.92 and 0.22 to 0.98, respectively. Overall, by comparing the results of the proposed models, ANFIS had the best performance which was followed by GRNN. The results of this study can be useful in managing the issues caused by dust storms and in the combating plans to desertification in the study regions.


Main Subjects

Abdolshahnejad, M., Khosravi, H., Nazari Samani, A. A., Zehtabia, G. R. & Alambaigi, M. (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 Farsi)
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.
Ansari Ghojghar, M. & Araghinejad, Sh. (2018). Investigating the effect of wind speed on the frequency of days with dust storms (Case study: Lorestan province). The fourth national conference on wind erosion and dust storms, Yazd.
 Araghinejad, S. (2013). Data-driven modeling: using MATLAB® in water resources and environmental engineering (Vol. 67). Springer Science & Business Media.
Araghinejad, Sh., Ansari Ghojghar, M., Pourgholam-Amiji., Liaghat, A & Bazrafshan, J. (2019). The Effect of Climate Fluctuation on Frequency of Dust Storms in Iran. Desert Ecosystem Engineering Journal, 7(21), 13-32. (In Persian)
Azizi, Gh., Shamsipour, A. A., Miri, M. & Safarrad, T., (2012). Dust analysis in southwestern Iran. Journal of Environmental Studies, 38(3), 123-134
Cao, R., Jiang, W., Yuan, L., Wang, W., Lv, Z., & Chen, Z. (2014). Inter-annual variations in vegetation and their response to climatic factors in the upper catchments of the Yellow River from 2000 to 2010. Journal of Geographical Sciences, 24(6), 963-979
Chen, S., Cowan, C. F. N. & Grant, P. M., (1991). Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks. IEEE Transactions on Neural Networks, 2(2), 302-309.
Dahiya, S., Singh, B., Gaur, S., Garg, V. K., & Kushwaha, H. S. (2007). Analysis of groundwater quality using fuzzy synthetic evaluation. Journal of Hazardous Materials, 147(3), 938-946.
Farajzadeh Asl, M. & Alizadeh, Kh. (2011). Spatial Analysis of Dust storm in Iran. The Journal of Spatial Planning, 15(1), 65-84 (In Persian)
Goudie, A. S., & Middleton, N. J. (2006). Desert dust in the global system. Springer Science & Business Media
Hahnenberger, M. & Nikoul, K. (2014). Geomorphic and land cover identification of dust sources in the eastern Great Basin of Utah, U.S.A. Journal of Geomorphology, 204 (2), 657-672.
Huang, M, Peng, G, Zhang, J, and Zhang, S. (2006). Application of artificial neural networks to the prediction of dust storms in Northwest Chin. Journal of Global and Planetary Change, 52, 216-224.
Ibarra-Berastegi, G., Elias, A., Barona, A., Saenz, J., Ezcurra, A., & de Argandoña, J. D. (2008). From diagnosis to prognosis for forecasting air pollution using neural networks: Air pollution monitoring in Bilbao. Environmental Modelling & Software, 23(5), 622-637.
Jamalizadeh Tajabadi, M. R., Moghaddamnia, A. R. & Piri, J. (2008). Investigating the ability of both artificial neural networks and supporting vector machines to predict dust storms in Zabol city. 4th National Conference on Watershed Management Sciences and Engineering, Management of watersheds. (In Farsi)
Jamalizadeh Tajabadi, M. R., Moghaddamnia, A. R., Piri, J. & Ekhtesasi, M. R. (2010). Application of artificial neural networks in dust storm prediction (case study: Zabol city). Iranian journal of Range and Desert Research, 17(2), 205-220. (In Farsi)
Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665–685
Karamouz, M., and Araghinejad, S. (2009). Advanced Hydrology. Amirkabir University Press. Tehran (In Persian)
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 Farsi)
Khashei, A., Shahidi, A., Pourrezabilondi, M., Amirabadizadeh, M. & Jafarzadeh, A. (2018). Performance Assessment of ANN and SVR for downscaling of daily rainfall in dry regions. Iranian Journal of Soil and Water Research, 49(4), 781-793.
Kim, D., Chin, M., Kemp, E. M., Tao, Z., Peters-Lidard, C. D., & Ginoux, P. (2017). Development of high-resolution dynamic dust source function-A case study with a strong dust storm in a regional model. Atmospheric environment, 159, 11-25.
Mohammadi, G, H., (2015). Analysis of Atmospheric Mechanisms in Dust Transport over West of Iran. Ph.D. thesis, Tabriz University, 142 pp. (In Farsi)
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
Rashki, A., Kaskaoutis, D. G., Goudie, A. S. and Kahn, R. A. (2013). Dryness of ephemeral lakes and consequences for dust activity: the case of the Hamoun drainage basin, southeastern Iran. Science of the Total Environment, 463, 552-564
Shao, Y., Wyrwoll, K. H., Chappell, A., Huang, J., Lin, Z., McTainsh, G. H. & Yoon, S. (2011). Dust cycle: An emerging core theme in Earth system science. Aeolian Research, 2(4), 181-204.
Sobhani, B., Salahi, B. & Goldust, A. (2015). Study the dust and evaluation of its possibility prediction based on statistical methods and ANFIS model in Zabol University. Geography and Development Iranian journal, 13(38), 123-138.  (In Farsi)
Specht, D. F. (1991). A general regression neural network, IEEE Transactions on Neural Networks, 2(6), 568-576.
Yarmoradi, Z., Nasiri, B., Mohammadi, Gh. H., & Karampour, M. (2018). Trend analysis of dusty day’s requency in Eastern arts o Iran associated with Climate Fluctuations. Desert Ecosystem Engineering Journal, 7(18), 1-14. (In Persian)
Zeinali, B., (2016). Investigation of frequency changes trend of days with dust storms in western half of Iran. Journal of Natural Environment hazards, 5(7), 100-87. (In Farsi)