Bridge afflux estimation using artificial intelligence systems

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dc.authorid0000-0002-3476-7512
dc.contributor.authorSeckin, Galip
dc.contributor.authorCobaner, Murat
dc.contributor.authorOzmen-Cagatay, Hatice
dc.contributor.authorAtabay, Serter
dc.contributor.authorErduran, Kutsi S.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2011
dc.departmentNiğde ÖHÜ
dc.description.abstractMost of the methods developed for the prediction of bridge afflux are generally based on either energy or momentum equations. Recent studies have shown that the energy method, which is one of the four bridge subroutines within the commonly used program HEC-RAS for computing water surface profiles along rivers, produced more accurate results than three other methods (momentum, WSPRO and Yarnell's methods) when applied to bridge afflux data obtained from experiments conducted in a two-stage channel. This work developed three artificial intelligence models (the radial basis neural network, the multi-layer perceptron and the adaptive neuro-fuzzy inference system) as alternatives to the energy method. Multiple linear and multiple non-linear regression models were also used in the analysis. The results showed that the performance of the adaptive neuro fuzzy inference system in predicting bridge afflux was superior to the other models.
dc.identifier.doi10.1680/wama.2011.164.6.283
dc.identifier.endpage293
dc.identifier.issn1741-7589
dc.identifier.issue6
dc.identifier.scopus2-s2.0-79957619685
dc.identifier.scopusqualityQ3
dc.identifier.startpage283
dc.identifier.urihttps://dx.doi.org/10.1680/wama.2011.164.6.283
dc.identifier.urihttps://hdl.handle.net/11480/4715
dc.identifier.volume164
dc.identifier.wosWOS:000291606400003
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherICE PUBL
dc.relation.ispartofPROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-WATER MANAGEMENT
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectfloods & floodworks
dc.subjectmodels (physical)
dc.subjectriver engineering
dc.titleBridge afflux estimation using artificial intelligence systems
dc.typeArticle

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