Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2124
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dc.contributor.authorTayfur, Gökmen-
dc.contributor.authorSingh, Vijay P.-
dc.date.accessioned2016-08-16T12:03:23Z
dc.date.available2016-08-16T12:03:23Z
dc.date.issued2006-12
dc.identifier.citationTayfur, G., and Singh, V. P. (2006). ANN and fuzzy logic models for simulating event-based rainfall-runoff. Journal of Hydraulic Engineering, 132(12), 1321-1330. doi:10.1061/(ASCE)0733-9429(2006)132:12(1321)en_US
dc.identifier.issn0733-9429
dc.identifier.issn0733-9429-
dc.identifier.urihttps://doi.org/10.1061/(ASCE)0733-9429(2006)132:12(1321)
dc.identifier.urihttp://hdl.handle.net/11147/2124
dc.description.abstractThis study presents the development of artificial neural network (ANN) and fuzzy logic (FL) models for predicting event-based rainfall runoff and tests these models against the kinematic wave approximation (KWA). A three-layer feed-forward ANN was developed using the sigmoid function and the backpropagation algorithm. The FL model was developed employing the triangular fuzzy membership functions for the input and output variables. The fuzzy rules were inferred from the measured data. The measured event based rainfall-runoff peak discharge data from laboratory flume and experimental plots were satisfactorily predicted by the ANN, FL, and KWA models. Similarly, all the three models satisfactorily simulated event-based rainfall-runoff hydrographs from experimental plots with comparable error measures. ANN and FL models also satisfactorily simulated a measured hydrograph from a small watershed 8.44 km2 in area. The results provide insights into the adequacy of ANN and FL methods as well as their competitiveness against the KWA for simulating event-based rainfall-runoff processes.en_US
dc.language.isoenen_US
dc.publisherAmerican Society of Civil Engineers (ASCE)en_US
dc.relation.ispartofJournal of Hydraulic Engineeringen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFuzzy setsen_US
dc.subjectKinematic wave theoryen_US
dc.subjectNeural networksen_US
dc.subjectRainfallen_US
dc.subjectRunoffen_US
dc.subjectSimulationen_US
dc.titleANN and fuzzy logic models for simulating event-based rainfall-runoffen_US
dc.typeArticleen_US
dc.authoridTR2054en_US
dc.institutionauthorTayfur, Gökmen-
dc.departmentİzmir Institute of Technology. Civil Engineeringen_US
dc.identifier.volume132en_US
dc.identifier.issue12en_US
dc.identifier.startpage1321en_US
dc.identifier.endpage1330en_US
dc.identifier.wosWOS:000242428800008en_US
dc.identifier.scopus2-s2.0-33751081243en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1061/(ASCE)0733-9429(2006)132:12(1321)-
dc.relation.doi10.1061/(ASCE)0733-9429(2006)132:12(1321)en_US
dc.coverage.doi10.1061/(ASCE)0733-9429(2006)132:12(1321)en_US
dc.identifier.scopusqualityQ1-
dc.identifier.wosqualityttpTop10%en_US
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.dept03.03. Department of Civil Engineering-
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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