Environmental Management Hazards

Environmental Management Hazards

Implementation of the Tri-Hybrid RBF-GA-SARIMA Meta-Model for Dust Storm Modeling (Case Study: Sistan and Baluchestan Province)

Document Type : Applied Article

Authors
Mohammad Ansari Ghojghar 1
Paria Pourmohammad 2
1 Assistant Professor, Department of Reclamation of arid and mountainous regions Engineering, Faculty of Natural Resources, University of Tehran, Iran
2 Ph.D. candidate, Department of Reclamation of arid and mountainous regions Engineering, Faculty of Natural Resources, University of Tehran, Iran
10.22059/jhsci.2025.387276.859
Abstract
This study evaluates the performance of the tri-hybrid RBF-GA-SARIMA meta-model in forecasting the frequency of dust storm days in Sistan and Baluchestan Province over a 50-year statistical period (1971–2020). The results obtained from this model were compared, using goodness-of-fit metrics, with those from individual models (SARIMA and RBF) and dual hybrid models (SARIMA-GA, RBF-GA, and RBF-SARIMA). All models demonstrated their peak accuracy and performance across all five stations during the fourth seasonal combination. However, substituting older seasonal data with one or two preceding seasons reduced accuracy and increased relative error in forecasting the FDSD index for the province. This phenomenon is attributed to the deposition of sand and dust particles during earlier seasons, followed by their subsequent mobilization by strong winds, triggering storms in later seasons. Among the evaluated models, the proposed hybrid tri-model exhibited superior accuracy and efficiency, emerging as the most effective approach for predicting the frequency of dust storm days. In contrast, the RBF-SARIMA dual hybrid model displayed the weakest performance, characterized by the lowest R-value and highest RMSE. These findings underscore that the integration of individual models does not inherently enhance the precision of climatic variable modeling.
Keywords
FDSD
Box-Jenkins
Seasonal Autoregressive
Genetic Algorithm
Artificial Neural Network
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Environmental Management Hazards
Volume 11, Issue 4
Winter 2025
Pages 305-322