Prediction of backwater level of bridge constriction using an artificial neural network

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dc.authorid0000-0003-4974-2833
dc.contributor.authorAtabay, Serter
dc.contributor.authorAbdalla, Jamal A.
dc.contributor.authorErduran, Kutsi S.
dc.contributor.authorMortula, Maruf
dc.contributor.authorSeckin, Galip
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2013
dc.departmentNiğde ÖHÜ
dc.description.abstractBridge constriction in channels usually increases the water level well above the normal depth and may result in overflow on the surrounding floodplain. In this paper, the experimental backwater level at which the maximum afflux value was observed due to bridge constriction was investigated. An artificial neural network (ANN) was used to predict the backwater level based on Manning's roughness coefficient of the main channel (n(mc)) and of the floodplain (n(fp)), bridge width (b) and flow discharge (Q). A multi-layer perceptron (MLP) ANN was used to predict the backwater level using these parameters. Multiple linear (MLR) regression and multiple non-linear regression (MNLR) were used as benchmarks for comparison of ANN results. It is concluded that an ANN can very accurately predict the backwater level. The developed ANN model was then used to conduct a parametric study to investigate the influence of n(mc), n(fp), b and Q on the backwater level due to a bridge constriction without piers. It is concluded that n(mc) and Q have a more profound effect on the backwater level than does n(fp), while b has very little effect on the backwater level within this range of parameters. Other observations and conclusions are also drawn.
dc.identifier.doi10.1680/wama.11.00112
dc.identifier.endpage570
dc.identifier.issn1741-7589
dc.identifier.issue10
dc.identifier.scopus2-s2.0-84893021392
dc.identifier.scopusqualityQ3
dc.identifier.startpage556
dc.identifier.urihttps://dx.doi.org/10.1680/wama.11.00112
dc.identifier.urihttps://hdl.handle.net/11480/4335
dc.identifier.volume166
dc.identifier.wosWOS:000325520200005
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherICE PUBLISHING
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.subjectbridges
dc.subjectfloods & floodworks
dc.subjectmathematical modelling
dc.titlePrediction of backwater level of bridge constriction using an artificial neural network
dc.typeArticle

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