Urmia lake water depth modeling using extreme learning machine-improved grey wolf optimizer hybrid algorithm

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dc.authoridGUL, ENES/0000-0001-9364-9738
dc.authoridVaheddoost, Babak/0000-0002-4767-6660
dc.authoridSafari, Mir Jafar Sadegh/0000-0003-0559-5261
dc.authorwosidGUL, ENES/AAH-6191-2021
dc.authorwosidVaheddoost, Babak/M-6824-2018
dc.authorwosidSafari, Mir Jafar Sadegh/A-4094-2019
dc.contributor.authorSales, Ali Kozekalani
dc.contributor.authorGul, Enes
dc.contributor.authorSafari, Mir Jafar Sadegh
dc.contributor.authorGhodrat Gharehbagh, Hadi
dc.contributor.authorVaheddoost, Babak
dc.date.accessioned2024-08-04T20:50:37Z
dc.date.available2024-08-04T20:50:37Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractLake water level changes are relatively sensitive to the climate-born events that rely on numerous phenomena, e.g., surface soil type, adjacent groundwater discharge, and hydrogeological situations. By incorporating the streamflow, groundwater, evaporation, and precipitation parameters into the models, Urmia lake water depth is simulated in the current study. For this, 40 years of streamflow and groundwater recorded data, respectively collected from 18 and 9 stations, are utilized together with evaporation and precipitation data from 7 meteorological stations. Extreme learning machine (ELM) is hybridized with four different optimizers, namely artificial bee colony (ABC), ant colony optimization for continuous domains (ACOR), whale optimization algorithm (WOA), and improved grey wolf optimizer (IGWO). In the analysis, 13 various scenarios with multiple input combinations are used to train and test the employed models. The best scenarios are then opted based on the performance metrics which are applied to assess the accuracy of the methods. According to the results, the hybrid ELM-IGWO shows better performance compared to the ELM-ABC, ELM-ACOR, and ELM-WOA approaches. Results indicate that the groundwater and persistence of the lake water depth have effective roles in models while incorporating higher number of variables can lower the performance of the models. Statistical analysis showed a 62% improvement in the performance of ELM-IGWO in comparison to the ELM-WOA with regard to the root mean square error. The promising outcomes obtained in this study may encourage the application of the utilized algorithms for modeling alternative hydrological problems.en_US
dc.identifier.doi10.1007/s00704-021-03771-1
dc.identifier.endpage849en_US
dc.identifier.issn0177-798X
dc.identifier.issn1434-4483
dc.identifier.issue1-2en_US
dc.identifier.scopus2-s2.0-85114322684en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage833en_US
dc.identifier.urihttps://doi.org/10.1007/s00704-021-03771-1
dc.identifier.urihttps://hdl.handle.net/11616/100185
dc.identifier.volume146en_US
dc.identifier.wosWOS:000692968200001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Wienen_US
dc.relation.ispartofTheoretical and Applied Climatologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLevel Fluctuationsen_US
dc.subjectColony Optimizationen_US
dc.subjectVector Machineen_US
dc.subjectPredictionen_US
dc.subjectClimateen_US
dc.subjectImpactsen_US
dc.subjectChinaen_US
dc.titleUrmia lake water depth modeling using extreme learning machine-improved grey wolf optimizer hybrid algorithmen_US
dc.typeArticleen_US

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