Iran-Water Resources Research

Iran-Water Resources Research

A Comprehensive Analysis of the Triple-Hybrid Metamodel of MLP-PSO-ARIMA for Forecasting the FDSD Index: A Case Study of Khuzestan Province

Document Type : Original Article

Author
Assistant Professor, Department of Rehabilitation of Arid and Mountainous Regions Engineering, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
Abstract
This study aims to evaluate the performance of the triple-hybrid metamodel of MLP-PSO-ARIMA in forecasting the frequency of dust storm days (FDSD) index across seven selected stations in Khuzestan Province during a 50-year statistical period (1970–2019). The results of the proposed triple-hybrid metamodel was compared against the standalone MLP and ARIMA models, as well as the hybrid models of MLP-PSO, ARIMA-PSO, and MLP-ARIMA, using performance metrics including R, RMSE, MAE, and NS. All the tested models demonstrated their highest accuracy during the first and second seasonal combinations. Accordingly, it was concluded that utilizing data from one or two preceding seasons yield more accurate predictions of the FDSD index for subsequent seasons in Khuzestan Province, whereas incorporating data from the third and fourth seasons does not enhance forecasting performance. Moreover, the multilayer perceptron (MLP) neural network outperformed the Box-Jenkins ARIMA model in predicting dust storm events in the region. While combining the MLP and ARIMA models improved the accuracy compared to their standalone counterparts, the improvement was not statistically significant. In contrast, the proposed triple-hybrid metamodel exhibited a statistically significant enhancement in accuracy over the dual-hybrid models.
 
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  • Receive Date 15 December 2024
  • Revise Date 15 April 2025
  • Accept Date 23 April 2025