The prediction of dust storm events using the integrated triple model GRNN-AF-ARMA and the Ensemble Kalman filter approach (Case Study: Khuzestan Province)

Ansari ghojghar, Mohammad; Tavoosi Rad, Ramtin

doi:10.61882/jeer.16.2.8

year 16, Issue 2 (Summer 2026) E.E.R. 2026, 16(2): 154-178 | Back to browse issues page

‎ 10.61882/jeer.16.2.8

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Ansari ghojghar M, Tavoosi Rad R. The prediction of dust storm events using the integrated triple model GRNN-AF-ARMA and the Ensemble Kalman filter approach (Case Study: Khuzestan Province). E.E.R. 2026; 16 (2) :154-178
URL: http://magazine.hormozgan.ac.ir/article-1-929-en.html

The prediction of dust storm events using the integrated triple model GRNN-AF-ARMA and the Ensemble Kalman filter approach (Case Study: Khuzestan Province)

Mohammad Ansari ghojghar ^*

, Ramtin Tavoosi Rad

Department of Reclamation of Arid and Mountainous regions Engineering, Faculty of Natural Resources, University of Tehran, Karaj, Iran. , ansari.ghojghar@ut.ac.ir

Abstract: (378 Views)

Accurate understanding of the temporal and spatial variations of destructive climatic phenomena is a fundamental step toward integrated management of these hazards. In recent years, the use of dual hybrid models for simulating dust storms has attracted significant attention. This study models the frequency index of dust storm days in Khuzestan province over a 50-year period (1971–2020). The performance of the triple hybrid model GRNN–AF–ARMA in predicting this index was compared with single models GRNN and ARMA, as well as dual hybrid models GRNN–AF, ARMA–AF, and GRNN–ARMA. The results demonstrated the superior performance of the triple hybrid model. Subsequently, to enhance prediction accuracy and stability, the Ensemble Kalman Filter (EnKF) was employed to correct and optimize the outputs of the hybrid model. The findings indicated that integrating this filter with the triple hybrid model significantly improved the correlation coefficient and the Nash–Sutcliffe efficiency. Therefore, the proposed model exhibits high capability in predicting the temporal and spatial patterns of dust storms in Khuzestan province. The outcomes of this research can serve as an effective tool for management planning and the implementation of preventive measures to mitigate damages caused by dust storms.

Keywords: Artificial Neural Network, Box-Jenkins Method, FDSD Index, Artificial Plants Algorithm, Modeling.

Full-Text [PDF 741 kb] (77 Downloads)

Type of Study: Research |
Received: 2026/04/21 | Published: 2026/08/16

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