Dam reservoir level modeling by neural network approach: A case study

dc.authorscopusid8904071600
dc.contributor.authorÜneş, Fatih
dc.date.accessioned2024-09-19T15:43:39Z
dc.date.available2024-09-19T15:43:39Z
dc.date.issued2010
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractPrediction of reservoir level fluctuation is important in the operation, design, and security of dams. In this paper, Artificial Neural Networks (ANN) is used for modeling. In such modeling approaches, it is possible to determine dam reservoir level and water balance (budget) by taking the monthly average precipitation and needed parameters into consideration. The basic data are available for over 29 years at the Tahtaköprü Dam in the southeast Mediterranean region of Turkey. As a sub-approach of ANN, a multi layer perceptron (MLP) is used. Bayesian regularization back-propagation training algorithm is employed for optimization of the network. MLP results are compared with the results of conventional multiple linear regression (MLR) and autoregressive (AR) models. The comparison shows that the ANN model provides better performance than the mentioned models in reservoir level estimation. ©ICS AS CR 2010.en_US
dc.identifier.endpage474en_US
dc.identifier.issn1210-0552
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-77956681949en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage461en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12483/14477
dc.identifier.volume20en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofNeural Network Worlden_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networks (ANNs)en_US
dc.subjectDamen_US
dc.subjectModelen_US
dc.subjectMulti layer perceptronen_US
dc.subjectPredictionen_US
dc.subjectReservoir levelen_US
dc.titleDam reservoir level modeling by neural network approach: A case studyen_US
dc.typeArticleen_US

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