Comparative analysis of neural network techniques for predicting water consumption time series

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dc.authoridFIRAT, MAHMUT/0000-0002-8010-9289
dc.authorwosidFIRAT, MAHMUT/ABG-7962-2020
dc.contributor.authorFirat, Mahmut
dc.contributor.authorTuran, Mustafa Erkan
dc.contributor.authorYurdusev, Mehmet Ali
dc.date.accessioned2024-08-04T20:32:18Z
dc.date.available2024-08-04T20:32:18Z
dc.date.issued2010
dc.departmentİnönü Üniversitesien_US
dc.description.abstractMonthly water consumption time series have been predicted using a series of Artificial Neural Network (ANN) techniques including Generalized Regression Neural Networks (GRNN), Cascade Correlation Neural Network (CCNN) and Feed Forward Neural Networks (FFNN). One hundred and eight data sets for the city of Izmir, Turkey are used for a number of ANN modeling exercises. Several ANN models depending on the combination of antecedent values of water consumption records are constructed and the best fit input structure is investigated. The performance of ANN models in training and testing stages are compared with the observed water consumption values to identify the best fit forecasting model based upon a number of selected performance criteria. (C) 2010 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.jhydrol.2010.01.005
dc.identifier.endpage51en_US
dc.identifier.issn0022-1694
dc.identifier.issn1879-2707
dc.identifier.issue1-2en_US
dc.identifier.scopus2-s2.0-77649187529en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage46en_US
dc.identifier.urihttps://doi.org/10.1016/j.jhydrol.2010.01.005
dc.identifier.urihttps://hdl.handle.net/11616/94989
dc.identifier.volume384en_US
dc.identifier.wosWOS:000276444700005en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Science Bven_US
dc.relation.ispartofJournal of Hydrologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectWater consumptionen_US
dc.subjectTime seriesen_US
dc.subjectANNen_US
dc.subjectGRNNen_US
dc.subjectFFNNen_US
dc.subjectCCNNen_US
dc.titleComparative analysis of neural network techniques for predicting water consumption time seriesen_US
dc.typeArticleen_US

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