Daily prediction of Urmia Lake water level using remote sensing data and honey badger optimization-based data-driven models

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dc.contributor.authorSaroughi, Mohsen
dc.contributor.authorKatipoglu, Okan Mert
dc.contributor.authorAkturk, Gaye
dc.contributor.authorGul, Enes
dc.contributor.authorSimsek, Oguz
dc.contributor.authorCitakoglu, Hatice
dc.date.accessioned2026-04-04T13:37:26Z
dc.date.available2026-04-04T13:37:26Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractArtificial neural networks (ANNs), support vector regression (SVR) and CatBoost regression (CBR) machine learning methods have been combined with the honey badger optimization algorithm (HBA) and metaheuristic optimization algorithm to accurately and reliably predict lake water level (LWL), which is of great importance for the management and planning of water resources. In this study, meteorological and hydrological parameters, including temperature (T), precipitation (P), date (D), surface soil moisture (SSW), root zone moisture (RZW) and water level (WL), were employed as input data for predicting the LWL of Urmia Lake. The input data were employed to develop six different prediction scenarios. This study not only examined the impact of meteorological and hydrological parameters on LWL prediction but also compared the performance of individual models and hybrid models. The Akaike information criterion (AIC) index was used to ascertain the optimal machine learning model and to evaluate the six prediction scenarios. The results of the study indicate that, according to the AIC index, the data regarding the water level (WL) were significant in the prediction models. However, it should be noted that satisfactory results could also be obtained without using the WL data in certain scenarios. In scenario 4 (input data: D, T, P, SSW, RZW), where the WL variable was not included, the HBA-CBR hybrid model was the best model with the lowest AIC value (Train: -63,735, Test:-4693). In prediction scenario 6 (input data: D, T, P, SSW, RZW, WL), which included the WL data, the HBA-SVR hybrid model demonstrated high performance with the lowest AIC value (Train: -102,358, Test:-27,233). Accordingly, it was recommended to use lagged WL values as input in WL prediction because the prediction accuracy of the models significantly improved. Furthermore, hybrid models were found to perform better than individual models due to their more consistent results.
dc.description.sponsorshipNASA's Global Modeling and Assimilation Office (GMAO)
dc.description.sponsorshipThe data used in the study were obtained from the NASA's Global Modeling and Assimilation Office (GMAO). Thanks to Grammarly Premium, which helps correct grammatical errors and spelling mistakes in the article.
dc.identifier.doi10.1007/s11600-024-01520-2
dc.identifier.endpage2933
dc.identifier.issn1895-6572
dc.identifier.issn1895-7455
dc.identifier.issue3
dc.identifier.orcid0000-0002-9477-7827
dc.identifier.orcid0000-0002-9811-5230
dc.identifier.orcid0000-0001-7319-6006
dc.identifier.orcid0000-0001-6324-0229
dc.identifier.scopus2-s2.0-105003762690
dc.identifier.scopusqualityQ2
dc.identifier.startpage2909
dc.identifier.urihttps://doi.org/10.1007/s11600-024-01520-2
dc.identifier.urihttps://hdl.handle.net/11616/109809
dc.identifier.volume73
dc.identifier.wosWOS:001415801900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Int Publ Ag
dc.relation.ispartofActa Geophysica
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectArtificial intelligence
dc.subjectLake water level
dc.subjectMetaheuristic optimization
dc.subjectRemote sensing
dc.titleDaily prediction of Urmia Lake water level using remote sensing data and honey badger optimization-based data-driven models
dc.typeArticle

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