A Comparative Evaluation of the Use of Artificial Neural Networks for Modeling the Rainfall-Runoff Relationship in Water Resources Management

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dc.authoridTURHAN, Evren/0000-0002-0742-4848
dc.contributor.authorTurhan, Evren
dc.date.accessioned2025-01-06T17:44:08Z
dc.date.available2025-01-06T17:44:08Z
dc.date.issued2021
dc.description.abstractRecently, Artificial Neural Network (ANN) methods, which have been successfully applied in many fields, have been considered for a large number of reliable streamflow estimation and modeling studies for the design and project planning of hydraulic structures. The present study aimed to model the rainfall-runoff relationship using different ANN methods. The Nergizlik Dam, located in the Seyhan sub-basin and one of the important basins in Turkey, was chosen as the study area. Analyses were carried out based on streamflow estimation with the help of observed precipitation and runoff data at certain time intervals. Feed Forward Backpropagation Neural Network (FFBPNN) and Generalized Regression Neural Network (GRNN) methods were adopted, and obtained results were compared with Multiple Linear Regression (MLR) method, which is accepted as the traditional method. Also, the models were performed using three different transfer functions to create optimum ANN modeling. As a result of the study, it was seen that ANN methods showed statistically good results in rainfall-runoff modeling, and the developed models can be successfully applied in the estimation of average monthly flows.
dc.identifier.doi10.12911/22998993/135775
dc.identifier.endpage178
dc.identifier.issn2299-8993
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85109903332
dc.identifier.scopusqualityQ2
dc.identifier.startpage166
dc.identifier.urihttps://doi.org/10.12911/22998993/135775
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2942
dc.identifier.volume22
dc.identifier.wosWOS:000669556000003
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPolish Soc Ecological Engineering-Ptie
dc.relation.ispartofJournal of Ecological Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectrainfall-runoff modeling
dc.subjectartificial neural networks methods
dc.subjectMLR
dc.subjectNergizlik Dam
dc.titleA Comparative Evaluation of the Use of Artificial Neural Networks for Modeling the Rainfall-Runoff Relationship in Water Resources Management
dc.typeArticle

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