Monthly water demand forecasting by adaptive neuro-fuzzy inference system approach; [Uyarlamali si̇ni̇rsel bulanik mantik yaklaşimi i̇le aylik su tüketi̇mi̇ni̇n tahmi̇ni̇]

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dc.contributor.authorFirat M.
dc.contributor.authorYurdusev M.A.
dc.contributor.authorMermer M.
dc.date.accessioned2024-07-22T08:22:17Z
dc.date.available2024-07-22T08:22:17Z
dc.date.issued2008
dc.description.abstractIn this study, an adaptive Neuro-Fuzzy inference system (ANFIS) is used to forecast monthly water use from several socio-economic and climatic factors, which affect water use. Totally 108 data sets are collected and data sets are divided into two subsets, training and testing. The models consisting of the combination of the independent variables are constructed and the best fit input structure is investigated. The performance of ANFIS models in training and testing sets are compared with the observations and the best fit model forecasting model is identified. For this purpose, some criteria of performance evaluation such as, Root Mean Square Error (RMSE), efficiency (E) and correlation coefficient (CORR) are calculated for all models. Then, the best fit models are also trained and tested by Multiple Regression (MR). The results of models are compared to get more reliable comparison. The results indicated that ANFIS can be applied successfully for monthly water demand forecasting.
dc.identifier.issn13001884
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/19012
dc.language.isoTurkish
dc.subjectBiochemical oxygen demand
dc.subjectCorrelation methods
dc.subjectFood processing
dc.subjectForecasting
dc.subjectFuzzy inference
dc.subjectFuzzy logic
dc.subjectReusability
dc.subjectAdaptive Neuro-Fuzzy Inference System (ANFIS)
dc.subjectBest fit
dc.subjectBest-fit models
dc.subjectClimatic factors
dc.subjectCorrelation coefficient (CC)
dc.subjectData sets
dc.subjectForecasting models
dc.subjectIndependent variables
dc.subjectMultiple regressions
dc.subjectPerformance evaluation (PE)
dc.subjectRoot mean-square error (RMSE)
dc.subjectSocio economic
dc.subjectTraining and testing
dc.subjectWater demands
dc.subjectWater uses
dc.subjectFuzzy systems
dc.titleMonthly water demand forecasting by adaptive neuro-fuzzy inference system approach; [Uyarlamali si̇ni̇rsel bulanik mantik yaklaşimi i̇le aylik su tüketi̇mi̇ni̇n tahmi̇ni̇]
dc.typeArticle

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