Performance enhancement of models through discrete wavelet transform for streamflow forecasting in Çarşamba River, Türkiye

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dc.authorid0000-0001-7547-847X
dc.authorid0000-0002-1870-3535
dc.contributor.authorTuğrul, Türker
dc.contributor.authorHınıs, Mehmet Ali
dc.date.accessioned2025-07-16T05:58:13Z
dc.date.available2025-07-16T05:58:13Z
dc.date.issued2025
dc.departmentMühendislik Fakültesi
dc.description.abstractStreamflow forecasts play an active role in hydrological planning and taking precautions against natural disasters. Streamflow prediction models are frequently used by scientists, especially in dam management, sustainable agriculture, flood control, and flood mitigation. Hence, streamflow prediction modeling was performed in this study, and six models were employed through four different machine learning (ML) algorithms, namely, the artificial neural network (ANN), random forest (RF), support vector machine (SVM), and decision tree (DT) that are well known in the literature, in order to predict the monthly streamflow of Çarşamba River, Türkiye. To further enhance model performance, wavelet transform (WT) was applied to ML algorithms. In this study, monthly average streamflow and precipitation data between 1974 and 2015 was used, and the minimum redundancy maximum relevance method (MRMR) and the cross-correlation method were performed to determine model input data. Results of this study revealed that RF had superiority over the other models before WT, followed by the SVM model. The SVM after WT (W-SVM), M04 (r: 0.9846, NSE: 0.9695, and RMSE: 0.3536) gave the most effective performance results, while the W-ANN model (r: 0.9797, NSE: 0.9588, and RMSE: 0.4108) showed the second best performance.
dc.identifier.doi10.2166/wcc.2025.709
dc.identifier.endpage756
dc.identifier.issn20402244
dc.identifier.issue2
dc.identifier.scopus86000587462
dc.identifier.startpage736
dc.identifier.urihttps://dx.doi.org/10.2166/wcc.2025.709
dc.identifier.urihttps://hdl.handle.net/20.500.12451/13304
dc.identifier.volume16
dc.identifier.wosWOS:001435579900013
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorHınıs, Mehmet Ali
dc.institutionauthorid0000-0002-1870-3535
dc.language.isoen
dc.publisherIWA Publishing
dc.relation.ispartofJournal of Water and Climate Change
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDam Management
dc.subjectRisk Assessment
dc.subjectSDI
dc.subjectStreamflow Forecasting
dc.subjectWavelet Transform
dc.titlePerformance enhancement of models through discrete wavelet transform for streamflow forecasting in Çarşamba River, Türkiye
dc.typeArticle

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