Modeling dimensionless longitudinal dispersion coefficient in natural streams using artificial intelligence methods

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dc.contributor.authorToprak, Z. Fuat
dc.contributor.authorHamidi, Nizamettin
dc.contributor.authorKisi, Ozgur
dc.contributor.authorGerger, Resit
dc.date.accessioned2024-04-24T16:02:45Z
dc.date.available2024-04-24T16:02:45Z
dc.date.issued2014
dc.departmentDicle Üniversitesien_US
dc.description.abstractLearning the Longitudinal Dispersion (LD) mechanism in natural channel is vitally important to be able to control water pollution and to prevent different stratification in flow that crucial for water resources conservation for both human and aquatic life. Many related studies can be found in the existing literature. However, almost all studies aim to investigate the mechanism of the LD in natural channels or to model dimensional LD coefficient. The main goal of this work is to develop three models based on different artificial neural network techniques to predict dimensionless (not dimensional) LD coefficients in natural channels. Another goal of this study is to present a large and critical assessment on the existing studies made on both dimensional and dimensionless LD. The data sets obtained from the literature concerning more than 30 rivers at different times in the United States of America, include the depth, the width, and the mean cross-sectional velocity of the flow, shear velocity, and dimensionless longitudinal dispersion coefficient. The results have been compared with the data at hand, a fuzzy based model, and seven conventional equations proposed in literature by using statistical magnitudes, error modes, and contour map method. It is observed that the feed forward neural network yields the best reliable results.en_US
dc.identifier.doi10.1007/s12205-014-0089-y
dc.identifier.endpage730en_US
dc.identifier.issn1226-7988
dc.identifier.issn1976-3808
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-84894877118en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage718en_US
dc.identifier.urihttps://doi.org/10.1007/s12205-014-0089-y
dc.identifier.urihttps://hdl.handle.net/11468/14901
dc.identifier.volume18en_US
dc.identifier.wosWOS:000332152700039
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherKorean Society Of Civil Engineers-Ksceen_US
dc.relation.ispartofKsce Journal of Civil Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDispersionen_US
dc.subjectFuzzy Logicen_US
dc.subjectDimensionless Longitudinal Dispersion Coefficienten_US
dc.subjectNeural Networksen_US
dc.subjectNatural Channelen_US
dc.titleModeling dimensionless longitudinal dispersion coefficient in natural streams using artificial intelligence methodsen_US
dc.typeArticleen_US

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