Investigation of flow resistance in smooth open channels using artificial neural networks

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dc.authorid0000-0002-2126-8757
dc.contributor.authorBilgil, A.
dc.contributor.authorAltun, H.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2008
dc.departmentNiğde ÖHÜ
dc.description.abstractAn accurate prediction of the friction coefficient is very important in hydraulic engineering since it directly affects the design of water structures, the calculation of velocity distribution, and an accurate determination of energy losses. However, conventional approaches that are profoundly based on empirical methods lack in providing high accuracy for the prediction of the friction coefficient. Consequently, new and accurate techniques are still highly demanded. This study introduces an efficient approach to estimate the friction coefficient via an artificial neural network, which is a promising computational tool in civil engineering. The estimated value of the friction coefficient is used in Manning Equation to predict the open channel flows in order to carry out a comparison between the proposed neural networks based approach and the conventional ones. Results show that the proposed approach is in good agreement with the experimental results when compared to the conventional ones. (C) 2008 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.flowmeasinst.2008.07.001
dc.identifier.endpage408
dc.identifier.issn0955-5986
dc.identifier.issue6
dc.identifier.scopus2-s2.0-53349179562
dc.identifier.scopusqualityQ2
dc.identifier.startpage404
dc.identifier.urihttps://dx.doi.org/10.1016/j.flowmeasinst.2008.07.001
dc.identifier.urihttps://hdl.handle.net/11480/5175
dc.identifier.volume19
dc.identifier.wosWOS:000261005700013
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherELSEVIER SCI LTD
dc.relation.ispartofFLOW MEASUREMENT AND INSTRUMENTATION
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectOpen channel
dc.subjectFriction coefficient
dc.subjectArtificial neural networks
dc.subjectManning equation
dc.titleInvestigation of flow resistance in smooth open channels using artificial neural networks
dc.typeArticle

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