Water use prediction by radial and feed-forward neural nets

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dc.contributor.authorYurdusev, MA
dc.contributor.authorFirat, M
dc.contributor.authorMermer, M
dc.contributor.authorTuran, ME
dc.date.accessioned2024-07-18T11:51:36Z
dc.date.available2024-07-18T11:51:36Z
dc.description.abstractIn this study, applicability of feed-forward and radial-basis neural networks for monthly water consumption prediction from several socio-economic and climatic factors affecting water use is investigated. A data set including a total of 108 data records is divided into two subsets: training and testing. Firstly, the models based on a single input variable are trained and tested by feed-forward and radial methods and feed-forward and radial performances of the models are compared. Then, the models based on multiple input variables are constructed according to performances of the models based on a single input variable. The performances of feed-forward and radial models in training and testing phases are compared with the observations and the best-fit model is identified. For this purpose, several criteria such as normalised root mean square error, efficiency and correlation coefficient are calculated for all models. Subsequently, the best-fit models are also trained and tested by multiple linear regression for comparison. The results indicated that feed-forward and radial methods can be applied successfully for monthly water consumption prediction.
dc.identifier.issn1741-7589
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/4975
dc.language.isoEnglish
dc.publisherTHOMAS TELFORD PUBLISHING
dc.subjectTIME-SERIES
dc.subjectDEMAND
dc.subjectNETWORK
dc.subjectRUNOFF
dc.subjectMODELS
dc.subjectFORECAST
dc.titleWater use prediction by radial and feed-forward neural nets
dc.typeArticle

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