Evaluating the Efficiency of Hybrid Metamodels of Machine Learning and Box Jenkins in Order to Model Dust Storms (Case Study: Khuzestan Province)

Document Type : Research Paper


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


The impact of dust phenomenon in Iran is so vast that it has involved more than half of the country's provinces in some way with the issues and limitations of this natural phenomenon. In addition to the environmental effects, it has disrupted the implementation of national sustainable development plans and so far, it has had many negative consequences. This research tries to present a new hybrid model using artificial intelligence hybrid metamodels as well as Box Jenkins hybrid metamodels to predict and model the FDSD index (frequency of days with dust storms), in seven synoptic stations of Khuzestan province with length The statistical period has been 40 years (1981-2020). The hybrid prediction algorithms used in this research include W-ANFIS, AF-SVM, ARIMA-NARX and SARIMA-SETAR. The prediction results showed that the decrease in the performance of hybrid models to predict the FDSD index has a direct relationship with the decrease in the frequency of days with dust storms. So that the correlation coefficient for experimental data in AF-SVM and W-ANFIS hypermodels from 0.991 and 0.985 to 0.985 and 0.958, respectively, and Nash Sutcliffe coefficient has also decreased from 0.977 and 0.960 to 0.973 and 0.952, respectively. Also, the RMSE coefficient from Abadan station to Dezful for the two metamodels from 0.135 and 0.151 to 0.140 and 0.179 respectively, And the MAE coefficient has also increased from 0.054 and 0.068 to 0.060 and 0.093, respectively. Correlation coefficient for test data in Box Jenkins SARIMA-SETAR and ARIMA-NARX hypermodels also from 0.967 and 0.951 to 0.958 and 0.941 respectively and the Nash Sutcliffe coefficient has also decreased from 0.945 and 0.923 to 0.938 and 0.913, respectively, which indicates the weakening of the performance of hybrid metamodels with the decrease in the frequency of dust storms in Khuzestan province. Also, by fitting four hybrid hypermodels on the FDSD index, it was shown that AF-SVM hybrid hypermodel had better performance than other methods. In a way, in all studied stations, the correlation coefficient and Nash-Sutcliffe coefficient are higher and the root mean square error coefficient and the mean absolute value of the error are lower, which shows the superiority of this hybrid meta-model over other meta-models for predicting the FDSD index in Khuzestan province. The results of this study can be used to model dust storms in other western regions of the country.


Abdolshahnejad, M., Khosravi, H., Nazari Samani, A., Zehtabian, G. and 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 Farsi)
Ahmadpour, A., Mirhashemi, S. and 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. doi: 10.29252/aridbiom.2021.15523.1828. (in Farsi)
AragiNejad, S.H. (2013). Data-driven modeling: using MATLAB® in water resources and environmental engineering (Vol. 67). Springer Science & Business Media, doi:10.1007/978-94-007-7506-0.
Asplin, B. R., Flottemesch, T. J. and Gordon, B. D. (2006). Developing models for patient flow and daily surge capacity research. Academic Emergency Medicine, 13(11): 1109-1113.
Atai, H. and Ahmadi, F. (2010). Investigating dust as one of the environmental problems of the Islamic world, a case study of Khuzestan province. The 4th International Congress of Geographers of the Islamic World, Zahedan. (In Farsi)
Bharlo, R. (2009). Forecasting time series with long-term dependencies using Narx Recurrent Neural Network. Twelfth Iranian Electrical Engineering Student Conference, Tabriz.
Box, G.E.P. and Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control. Revised Edition, Holden-Day, PP 324.
Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (1994). Time series Analysis: Forecasting and Control. 3rdEd. prentice Hall, Englewood Cliffs Inc., New Jersey. 598p.
Cartlidge, J.P. and Bulloc, S.G. (2004). Combating coevolutionary disengagement by reducing parasite virulence. Evolutionary Computation, 12(2), 193-222.
Cheng, L., Wu, X. and Wang, Y. (2018). Artificial Flora (AF) optimization algorithm. Applied Science, 329(8), 2- 22.
Cochrane, J. H. (2005). Time series for macroeconomics and finance. Manuscript, University of Chicago, 1-136.
Dehghani, R., Torabi poudeh, H., Younesi, H. and SHahinejad, B. (2020). Aplication of the Hybrid Model of Support Vector Machine-Algorithm Artificial Flora in Estimating the Daily Flow of Rivers (Case study: Dez basin). Iran-Water Resources Research, 16(2), 132-149.
Eskandari, A., Solgi, A. and Zarei, H. (2018). Simulating Fluctuations of Groundwater Level Using a Combination of Support Vector Machine and Wavelet Transform. Irrigation Sciences and Engineering, 41(1), 165-180. doi: 10.22055/jise.2018.13577.
Goudie, A. S. and Middleton, N.J. (2006). Desert dust in the global system. Springer Science & Business Media.
Hillis, W.D. (1990). Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D: Nonlinear Phenomena, 42, 228–234.
Jacquelyn, C. (2009). Climate analysis and longrange forecasting of dust storms in Iraq, (Dissertation for the degree of Master of Science), Graduate college of Naval postgraduate academy, Monterey California.
Jamalizadeh Tajabadi, M., Moghadam nia, A., piri, J. and Ekhtesasi, M. (2010). Application of artificial neural networks in dust storm prediction (case study: Zabol city). Iranian Journal of Rangeland 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. doi:10.1109/21.256541.
Jokar, N., Charkhabi, A., Mohseni, H., Jafari, S. and Gandami, Z. (2012). Investigating the origin and direction of sandstorms in Khuzestan. The first international conference on the phenomenon of dust and dealing with its harmful effects, 15-17 February, Khuzestan. (In Farsi)
keykhosravi, S., Nejadkoorki, F. and 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. doi: 10.22038/jreh.2019.38083.1277. (In Farsi)
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. and Tapper, N. J. (2014). The Dust Storm Index (DSI): a method for monitoring broadscale wind erosion using meteorological records. Aeolian Research, 12, 29-40.
Omidvar, K., Nabavizadeh, M., Samarehghasem, M. (2015). Assessment of NARX Neural Network in Prediction of Daily Precipitation in Kerman Province. Physical Geography Quarterly, 8(27), 73-90.
Pagie, L. and Mitchell, M.A. (2002). Comparison of evolutionary and coevolutionary search. International Journal of Computational Intelligence and Application, 2, 53–69.
Pourgholam Amiji, M., Ansari Ghojghar, M., Bazrafshan, J., Liaghat, A. and 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. doi: 10.22059/jphgr.2021.303847.1007524. (In Farsi)
Qing, C., Ewing, B. T. and Thompson, M. A. (2012). Forecasting wind speed with recurrent neural networks. European Journal of Operational Research, Volume 221(1), 148-154.
Rosin, C.D. and Belew, R.K. (1995). Methods for competitive co-evolution. Finding Opponents Worth Beating in Proceedings of the International Conference on Genetic Algorithms Pittsburgh, 373–381.
Selajgah, A., Fathabadi, A. and Najafi Hajivar, M. (2008). Comparison of neural network and time series in drought forecasting (case study: Razavi Khorasan province). Iranian Journal of Watershed Science and Engineering, 2(4), 74-77. (In Farsi)
Shaker Sureh, F. and Asadi, E. (2019). Meteorological and hydro-logical drought communication in Salmas Plain. DEEJ, 8 (22), 89-100. (In Farsi)
Sobhani, B. and Safarian zengir, V. (2020). Analysis and prediction of Dust phenomenon in the southwest of Iran. Journal of Natural Environmental Hazards, 8(22), 179-198. doi: 10.22111/jneh.2019.28148.1481. (In Farsi)
Tong, H. (1983). Threshold Models in Non-Linear Time Series Analysis. Springer, New York.
Wang, D., Safavi, A.A. and Romagnoli, J.A. (2000). Wavelet based adaptive robust M-estimator for non-linear system identification. AIChE Journal, 46(4), 1607- 1615. 
Wiegand, R.P. and Sarma, J. (2004). Spatial Embedding and loss of gradient in cooperative coevolutionary algorithms. In roceedings of the International Conference on Parallel Problem Solving from Nature, Berlin Germany, 43, 912–921.
Williams, N. and Mitchell, M. (2005). Investigating the success of spatial coevolution. In Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, Washington, 46, 523–530.
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)