A Comparison of MLR and Three Different Artificial Neural Networks Techniques for Daily Mean Flow Prediction

dc.contributor.author Birinci, V.
dc.date.accessioned 2024-10-15T20:19:20Z
dc.date.available 2024-10-15T20:19:20Z
dc.date.issued 2009
dc.department Okan University en_US
dc.department-temp [Birinci, V.] Okan Univ, Fac Engn, Dept Civil Engn, Istanbul, Turkey en_US
dc.description.abstract Prediction for hydrologic events has always been an important issue for optimizing and planning the whole system. In this study, a conventional multivariate linear regression method and three different ANN (Artificial Neural Networks) techniques (Feed Forward Back Propagation, Generalized Regression and Radial Basis Function) were used to predict and model daily mean flow of Anamur River. Because of the climate change, last 5234 days' data between the years 1989 and 2003 were processed and those three techniques of ANN and the conventional method MLR (multivariate linear regression) were compared to each other. The best results were obtained with FFBP algorithm. en_US
dc.description.woscitationindex Conference Proceedings Citation Index - Science
dc.identifier.citationcount 0
dc.identifier.endpage + en_US
dc.identifier.isbn 9789604741427
dc.identifier.startpage 61 en_US
dc.identifier.uri https://hdl.handle.net/20.500.14517/6439
dc.identifier.wos WOS:000276620800009
dc.institutionauthor Birinci, V.
dc.language.iso en
dc.publisher World Scientific and Engineering Acad and Soc en_US
dc.relation.ispartof 7th WSEAS International Conference on Environment, Ecosystems and Development -- DEC 14-16, 2009 -- Puerto de la Cruz, SPAIN en_US
dc.relation.ispartofseries Energy and Environmental Engineering Series
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject ANN en_US
dc.subject MLR en_US
dc.subject daily flow en_US
dc.subject GRNN en_US
dc.subject FFBPNN en_US
dc.subject RBFNN en_US
dc.title A Comparison of MLR and Three Different Artificial Neural Networks Techniques for Daily Mean Flow Prediction en_US
dc.type Conference Object en_US
dc.wos.citedbyCount 0

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