Forecasting daily streamflow discharges using various neural network models and training algorithms

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dc.contributor.authorNacar, Sinan
dc.contributor.authorHinis, M. Ali
dc.contributor.authorKankal, Murat
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-29T19:29:53Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-29T19:29:53Z
dc.date.issued2018
dc.departmentMühendislik Fakültesi
dc.description.abstractStreamflow forecasting based on past records is an important issue in both hydrologic engineering and hydropower reservoir management. In the study, three artificial Neural Network (NN) models, namely NN with well-known multi-layer perceptron (MLPNN), NN with principal component analyses (PCA-NN), and NN with time lagged recurrent (TLR-NN), were used to 1, 3, 5, 7, and 14 ahead of daily streamflow forecast. Daily flow discharges of Haldizen River, located in the Eastern Black Sea Region, Turkey the time period of 1998-2009 was used to forecast discharges. Backpropagation (BP), Conjugate Gradient (CG), and Levenberg-Marquardt (LM) were applied to the models as training algorithm. The result demonstrated that, firstly, the forecast ability of CG algorithm much better than BP and LM algorithms in the models; secondly, the best performance was obtained by PCA-NN and MLP-NN for short time (1, 3, and 5 day-ahead) forecast and TLR-NN for long time (7 and 14 day-ahead) forecast.
dc.identifier.doi10.1007/s12205-017-1933-7
dc.identifier.endpage3685en_US
dc.identifier.issn1226-7988
dc.identifier.issn1976-3808
dc.identifier.issue9en_US
dc.identifier.scopusqualityQ2
dc.identifier.startpage3676en_US
dc.identifier.urihttps://doi.org/10.1007/s12205-017-1933-7
dc.identifier.urihttps://hdl.handle.net/20.500.12451/6218
dc.identifier.volume22en_US
dc.identifier.wosWOS:000441994400049
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherKorean Society of Civil Engineers
dc.relation.ispartofKSCE Journal of Civil Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDaily Streamflow Forecasting
dc.subjectArtificial Neural Network
dc.subjectPrincipal Component Analyses
dc.subjectTime Lagged Recurrent
dc.titleForecasting daily streamflow discharges using various neural network models and training algorithms
dc.typeArticle

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