Optimizing river flow rate predictions: integrating cognitive approaches and meteorological insights

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dc.authoridKartal, Veysi/0000-0003-4671-1281
dc.contributor.authorKartal, Veysi
dc.contributor.authorKarakoyun, Erkan
dc.contributor.authorAkiner, Muhammed Ernur
dc.contributor.authorKatipoglu, Okan Mert
dc.contributor.authorKuriqi, Alban
dc.date.accessioned2024-12-24T19:24:42Z
dc.date.available2024-12-24T19:24:42Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractThe models used in this study make it possible to make more accurate predictions about river discharge. These results can influence flood protection strategies, water resource management, and hydropower generation. Due to their ability to capture the underlying temporal relationships in the data, time series forecasts have become increasingly popular in recent years. This study examines the critical processes in river forecasting for the Kizilirmak River basin. We begin with a look at data collection and preparation, followed by an overview of time series forecasting models. Finally, we look at the process of model testing and selection. Seven techniques were used to predict streamflow from meteorological data: Artificial Neural Network (ANN), Firefly-based ANN (FFA-ANN), Random Forest (RF), K-Nearest Neighbors (KNN), Generalized Linear Regression (GLR), Support Vector Machines (SVM), Least Squares Boosted Trees (LSBT). The performance of the models was evaluated using the statistical indicators. The LSBT, RF, and ANN models provided the best results for Kayseri, K & imath;r & scedil;ehir, and Gemerek stations, respectively. The RF, ANN and GLR models provided second best results for these stations, respectively.
dc.description.sponsorshipState Water Works; Foundation for Science and Technology [UIDB/04625/2020]
dc.description.sponsorshipSpecial thanks to the General Directorate of Meteorology (MGM) and State Water Works (DSI) for providing the database used in this study. Alban Kuriqi is grateful for the Foundation for Science and Technology's support through funding UIDB/04625/2020 from the research unit CERIS. We would like to thank the free version of the DeepL translator, and premium version Grammarly which helped translate and correction of English errors of part of this work.
dc.identifier.doi10.1007/s11069-024-07043-9
dc.identifier.issn0921-030X
dc.identifier.issn1573-0840
dc.identifier.scopus2-s2.0-85209759444
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s11069-024-07043-9
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6108
dc.identifier.wosWOS:001360285600001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofNatural Hazards
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectIntelligent algorithms
dc.subjectMeteorological data
dc.subjectRiver flow rate
dc.subjectTime series forecasting
dc.titleOptimizing river flow rate predictions: integrating cognitive approaches and meteorological insights
dc.typeArticle

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