Optimizing Extreme Learning Machine for Drought Forecasting: Water Cycle vs. Bacterial Foraging

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dc.authoridGUL, ENES/0000-0001-9364-9738
dc.authoridMohammadi, Babak/0000-0001-8427-5965
dc.authoridDANANDEH MEHR, ALI/0000-0003-2769-106X
dc.authoridMohammadi, Babak/0000-0001-8427-5965
dc.authoridNourani, Vahid/0000-0002-6931-7060
dc.authoridShoaie, Shahrokh/0009-0005-6098-3146
dc.authorwosidGUL, ENES/AAH-6191-2021
dc.authorwosidMohammadi, Babak/JCO-4552-2023
dc.authorwosidDANANDEH MEHR, ALI/S-9321-2017
dc.authorwosidTUR, Rifat/C-3512-2016
dc.authorwosidMohammadi, Babak/G-4012-2018
dc.authorwosidNourani, Vahid/F-4051-2017
dc.contributor.authorMehr, Ali Danandeh
dc.contributor.authorTur, Rifat
dc.contributor.authorAlee, Mohammed Mustafa
dc.contributor.authorGul, Enes
dc.contributor.authorNourani, Vahid
dc.contributor.authorShoaei, Shahrokh
dc.contributor.authorMohammadi, Babak
dc.date.accessioned2024-08-04T20:53:33Z
dc.date.available2024-08-04T20:53:33Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractMachine learning (ML) methods have shown noteworthy skill in recognizing environmental patterns. However, presence of weather noise associated with the chaotic characteristics of water cycle components restricts the capability of standalone ML models in the modeling of extreme climate events such as droughts. To tackle the problem, this article suggests two novel hybrid ML models based on combination of extreme learning machine (ELM) with water cycle algorithm (WCA) and bacterial foraging optimization (BFO). The new models, respectively called ELM-WCA and ELM-BFO, were applied to forecast standardized precipitation evapotranspiration index (SPEI) at Beypazari and Nallihan meteorological stations in Ankara province (Turkey). The performance of the proposed models was compared with those the standalone ELM considering root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and graphical plots. The forecasting results for three- and six-month accumulation periods showed that the ELM-WCA is superior to its counterparts. The NSE results of the SPEI-3 forecasting in the testing period proved that the ELM-WCA improved drought modeling accuracy of the standalone ELM up to 72% and 85% at Beypazari and Nallihan stations, respectively. Regarding the SPEI-6 forecasting results, the ELM-WCA achieved the highest RMSE reduction percentage about 63% and 56% at Beypazari and Nallihan stations, respectively.en_US
dc.identifier.doi10.3390/su15053923
dc.identifier.issn2071-1050
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85151128508en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/su15053923
dc.identifier.urihttps://hdl.handle.net/11616/101258
dc.identifier.volume15en_US
dc.identifier.wosWOS:000946902100001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofSustainabilityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectdroughten_US
dc.subjectSPEIen_US
dc.subjectextreme learning machineen_US
dc.subjectwater cycleen_US
dc.subjectbacterial forgingen_US
dc.subjectoptimizationen_US
dc.titleOptimizing Extreme Learning Machine for Drought Forecasting: Water Cycle vs. Bacterial Foragingen_US
dc.typeArticleen_US

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