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dc.contributor.authorÜneş, Fatih
dc.contributor.authorDemirci, Mustafa
dc.contributor.authorTaşar, Bestami
dc.contributor.authorKaya, Yunus Ziya
dc.contributor.authorVarçin, Hakan
dc.date.accessioned12.07.201910:50:10
dc.date.accessioned2019-07-12T22:05:55Z
dc.date.available12.07.201910:50:10
dc.date.available2019-07-12T22:05:55Z
dc.date.issued2019
dc.identifier.citationUnes, F., Demirci, M., Tasar, B., Kaya, Y. Z., Varcin, H. (2019). Estimating dam reservoir level fluctuations using data-driven techniques. Polish Journal of Environmental Studies, 28(5), 3451-3462. doi: 10.15244/pjoes/93923en_US
dc.identifier.issn1230-1485
dc.identifier.issn2083-5906
dc.identifier.urihttps://doi.org/10.15244/pjoes/93923
dc.identifier.urihttps://hdl.handle.net/20.500.12508/587
dc.descriptionWOS: 000469277000041en_US
dc.descriptionScience Citation Index Expandeden_US
dc.description.abstractEstimating dam reservoir level is very important in terms of the operation of a dam, the safety of transport in the river, the design of hydraulic structures, and determining pollution, the salinity of the river flow fluctuations and the change of water quality in the dam reservoir. In this study, an adaptive network-based fuzzy inference system (ANFIS), support vector machines (SVM), radial basis neural networks (RBNN) and generalized regression neural networks (GRNN) approaches were used for the prediction and estimation of daily reservoir levels of Millers Ferry Dam on the Alabama River in the USA. Particularly, the feasibility of ANFIS as a prediction model for the reservoir level has been investigated. The Millers Ferry Dam on the Alabama River in the USA was selected as a case study area to demonstrate the feasibility and capacity of ANFIS, SVM, RBNN, and GRNN. The model results are compared with conventional auto-regressive models (AR), auto-regressive moving average (ARMA), multi-linear regression (MLR) models, and artificial intelligence models for the best-input combinations. The comparison results show that ANFIS models give better results than classical and other artificial intelligence models in estimating reservoir level.en_US
dc.language.isoengen_US
dc.publisherHarden_US
dc.relation.isversionof10.15244/pjoes/93923en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectReservoir Levelen_US
dc.subjectPredictionen_US
dc.subjectAdaptive Network-Based Fuzzy Inference Systemen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectRadial Basis Neural Networksen_US
dc.subjectGeneralized Regression Neural Networksen_US
dc.subject.classificationEnvironmental Sciencesen_US
dc.subject.otherPredictionen_US
dc.subject.otherFuzzyen_US
dc.subject.otherNetworksen_US
dc.titleEstimating dam reservoir level fluctuations using data-driven techniquesen_US
dc.typearticleen_US
dc.relation.journalPolish Journal of Environmental Studiesen_US
dc.contributor.departmentMühendislik ve Doğa Bilimleri Fakültesien_US
dc.identifier.volume28en_US
dc.identifier.issue5en_US
dc.identifier.startpage3451en_US
dc.identifier.endpage3462en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.isteauthorÜneş, Fatih
dc.contributor.isteauthorDemirci, Mustafa
dc.contributor.isteauthorTaşar, Bestami
dc.contributor.isteauthorVarçin, Hakan
dc.relation.indexWeb of Scienceen_US


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