An improved adaptive neuro-fuzzy inference system for hydrological drought prediction in Algeria

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dc.authoridJehanzaib, Muhammad/0000-0002-5556-3738
dc.authoridGUL, ENES/0000-0001-9364-9738
dc.authoridDANANDEH MEHR, ALI/0000-0003-2769-106X
dc.authoridMohammadi, Babak/0000-0001-8427-5965
dc.authoridACHITE, Mohammed/0000-0001-6084-5759
dc.authorwosidJehanzaib, Muhammad/ABC-1316-2020
dc.authorwosidGUL, ENES/AAH-6191-2021
dc.authorwosidDANANDEH MEHR, ALI/S-9321-2017
dc.authorwosidMohammadi, Babak/JCO-4552-2023
dc.authorwosidAchite, Mohammed/GLS-8333-2022
dc.contributor.authorAchite, Mohammed
dc.contributor.authorGul, Enes
dc.contributor.authorElshaboury, Nehal
dc.contributor.authorJehanzaib, Muhammad
dc.contributor.authorMohammadi, Babak
dc.contributor.authorMehr, Ali Danandeh
dc.date.accessioned2024-08-04T20:54:34Z
dc.date.available2024-08-04T20:54:34Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractDrought has negative impacts on water resources, food security, soil degradation, desertification and agricultural productivity. The meteorological and hydrological droughts prediction using standardized precipitation/runoff indices (SPI/SRI) is crucial for effective water resource management. In this study, we suggest ANFISWCA, an adaptive neuro-fuzzy inference system (ANFIS) optimized by the water cycle algorithm (WCA), for hydrological drought forecasting in semi-arid regions of Algeria. The new model was used to predict SRI at 3-, 6-, 9-, and 12 -month accumulation periods in the Wadi Mina basin, Algeria. The results of the model were assessed using four criteria; determination coefficient, mean absolute error, variance accounted for, and root mean square error, and compared with those of the standalone ANFIS model. The findings suggested that throughout the testing phase at all the sub-basins, the proposed hybrid model outperformed the conventional model for estimating drought. This study indicated that the WCA algorithm enhanced the ANFIS model's drought forecasting accuracy. The pro-posed model could be employed for forecasting drought at multi-timescales, deciding on remedial strategies for dealing with drought at study stations, and aiding in sustainable water resources management.en_US
dc.description.sponsorshipLund Universityen_US
dc.description.sponsorshipOpen access funding provided by Lund University.en_US
dc.identifier.doi10.1016/j.pce.2023.103451
dc.identifier.issn1474-7065
dc.identifier.issn1873-5193
dc.identifier.scopus2-s2.0-85166481383en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1016/j.pce.2023.103451
dc.identifier.urihttps://hdl.handle.net/11616/101495
dc.identifier.volume131en_US
dc.identifier.wosWOS:001052900300001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofPhysics and Chemistry of The Earthen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHydrological droughten_US
dc.subjectHybrid modelen_US
dc.subjectANFISen_US
dc.subjectWater cycle algorithmen_US
dc.subjectsemi -arid regionsen_US
dc.titleAn improved adaptive neuro-fuzzy inference system for hydrological drought prediction in Algeriaen_US
dc.typeArticleen_US

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