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dc.contributor.authorTombul, Mustafa
dc.contributor.authoroğul, Ersin
dc.contributor.editorHuang, DS
dc.contributor.editorLi, K
dc.contributor.editorIrwin, GW
dc.date.accessioned2019-10-21T21:11:37Z
dc.date.available2019-10-21T21:11:37Z
dc.date.issued2006
dc.identifier.isbn3-540-37255-5
dc.identifier.issn0170-8643
dc.identifier.urihttps://hdl.handle.net/11421/21084
dc.descriptionInternational Conference on Intelligent Computing (ICIC) -- AUG 16-19, 2006 -- Kunming, PEOPLES R CHINAen_US
dc.descriptionWOS: 000240383400038en_US
dc.description.abstractThe artificial neural networks (ANNs) have been applied to various hydrologic problems in recently. In this paper, the artificial neural network (ANN) model is employed in the application of rainfall-runoff process on a semi-arid catchment, namely the Kurukavak catchment. The Kurukavak catchment, a sub-basin of the Sakarya basin in NW Turkey, has a drainage area of 4.25 km(2). The performance of the developed neural network based model was compared with multiple linear regression based model using the same observed data. It was found that the neural network model consistently gives good predictions. The conclusion is drawn that the ANN model can be used for prediction of flow for small semi-arid catchments.en_US
dc.description.sponsorshipIEEE Computat Intelligence Soc, Int Neural Network Soc, Natl Sci Fdn Chinaen_US
dc.language.isoengen_US
dc.publisherSpringer-Verlag Berlinen_US
dc.relation.ispartofseriesLECTURE NOTES IN CONTROL AND INFORMATION SCIENCES
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleModeling of rainfall-runoff relationship at the semi-arid small catchments using artificial neural networksen_US
dc.typeconferenceObjecten_US
dc.relation.journalIntelligent Control and Automationen_US
dc.contributor.departmentAnadolu Üniversitesi, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.identifier.volume344en_US
dc.identifier.startpage309en_US
dc.identifier.endpage318en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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