Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria

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dc.authoridJehanzaib, Muhammad/0000-0002-5556-3738
dc.authoridKartal, Veysi/0000-0003-4671-1281
dc.authoridACHITE, Mohammed/0000-0001-6084-5759
dc.contributor.authorAchite, Mohammed
dc.contributor.authorKatipoglu, Okan Mert
dc.contributor.authorJehanzaib, Muhammad
dc.contributor.authorElshaboury, Nehal
dc.contributor.authorKartal, Veysi
dc.contributor.authorAli, Shoaib
dc.date.accessioned2024-12-24T19:33:32Z
dc.date.available2024-12-24T19:33:32Z
dc.date.issued2023
dc.departmentSiirt Üniversitesi
dc.description.abstractDrought is one of the most severe climatic calamities, affecting many aspects of the environment and human existence. Effective planning and decision making in disaster-prone areas require accurate and reliable drought predictions globally. The selection of an effective forecasting model is still challenging due to the lack of information on model performance, even though data-driven models have been widely employed to anticipate droughts. Therefore, this study investigated the application of simple extreme learning machine (ELM) and wavelet-based ELM (W-ELM) algorithms in drought forecasting. Standardized runoff index was used to model hydrological drought at different timescales (1-, 3-, 6-, 9-, and 12-month) at five Wadi Mina Basin (Algeria) hydrological stations. A partial autocorrelation function was adopted to select lagged input combinations for drought prediction. The results suggested that both algorithms predict hydrological drought well. Still, the performance of W-ELM remained superior at most of the hydrological stations with an average coefficient of determination = 0.74, root mean square error = 0.36, and mean absolute error = 0.43. It was also observed that the performance of the models in predicting drought at the 12-month timescale was higher than at the 1-month timescale. The proposed hybrid approach combined ELM's fast-learning ability and discrete wavelet transform's ability to decompose into different frequency bands, producing promising outputs in hydrological droughts. The findings indicated that the W-ELM model can be used for reliable drought predictions in Algeria.
dc.identifier.doi10.3390/atmos14091447
dc.identifier.issn2073-4433
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85172909986
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/atmos14091447
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8188
dc.identifier.volume14
dc.identifier.wosWOS:001076952100001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofAtmosphere
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectstandardized runoff index
dc.subjectdrought indices
dc.subjectextreme learning machine
dc.subjectwavelet transform
dc.subjectAlgeria
dc.titleHydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria
dc.typeArticle

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