A comparison of the ELM algorithm and XBeach model in developing a hybrid XBeach-ELM method for dust storm prediction (Case study: Lorestan Province)

Articles in Press

Document Type : Research Paper

Authors

1 Assistant professor, Department of reclamation of arid and mountainous regions Engineering, Faculty of Natural Resources, University of Tehran, Karaj, Iran.

2 Ph.D. candidate, Department of Reclamation of arid and mountainous regions Engineering, Faculty of Natural Resources, University of Tehran,Karaj, Iran.

Abstract

This study aims to assess and compare the performance of the Extreme Learning Machine (ELM) rapid learning algorithm with the open-source numerical model XBeach across ten synoptic stations in Lorestan Province (including Nurabad, Alishtar, Borujerd, Kuhdasht, Khorramabad, Pol-e Dokhtar, Nojian, Dorud, Azna, and Aligudarz) over a 50-year period (1971–2020), with the objective of proposing a hybrid XBeach-ELM method for predicting the Frequency of Dust Storm Days (FDSD) index. The findings revealed a statistically significant and meaningful difference in the modeling results when using the hybrid XBeach-ELM approach compared to other methods examined. The hybrid XBeach-ELM method outperformed the ELM and XBeach models, showing the lowest values for the NRMSE and MAPE error metrics. A t-test comparison of the observed and predicted mean values confirmed the acceptance of the null hypothesis, indicating no significant difference between the observed and predicted time series for the FDSD index when applying the hybrid XBeach-ELM method in Lorestan Province. This equivalence was not observed with the individual ELM or XBeach models. These results suggest that only the hybrid model effectively preserved the mean of the observed time series in predicting the FDSD index. The outcomes of this study have substantial implications for the enhancement of early warning systems, enabling more accurate dust storm forecasting, reducing human and economic losses, supporting urban and infrastructure planning to bolster resilience in high-risk areas, informing local and national policy-making on dust storm management, and advancing the development of sustainable solutions to improve the accuracy of predictive models.

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Iranian Journal of Soil and Water Research

Articles in Press, Accepted Manuscript
Available Online from 21 April 2025

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