Firat M.Yurdusev M.A.Turan M.E.2024-07-222024-07-22200909204741http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/18957Various Artificial Neural Network techniques such as Generalized Regression Neural Networks (GRNN), Feed Forward Neural Networks (FFNN) and Radial Basis Neural Networks (RBNN) have been evaluated based on their performance in forecasting monthly water consumptions from several socio-economic and climatic factors, which affect water use. The data set including total 108 data records is 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 ANN models in training and testing stages are compared with the observed water consumption values to identify the best fit forecasting model. For this purpose, some performance criteria such as Normalized Root Mean Square Error (NRMSE), efficiency (E) and correlation coefficient (CORR) are calculated for all models. The best fit models are also trained and tested by Multiple Linear Regression (MLR). The results indicated that GRNN outperforms all other methods in modeling monthly water consumptions. © Springer Science+Business Media B.V. 2008.EnglishBackpropagationForecastingStatistical testsWater managementWater supplyartificial neural networkclimate effectcorrelationefficiency measurementforecasting methodmultiple regressionperformance assessmentregression analysissocioeconomic conditionswater useANNArtificial neural networksBest fitsBest-fit modelsClimatic factorsCorrelation coefficientsData recordsData setsDemand forecastingFeed-forward neural networksForecasting modelsGeneralized regression neural networksGRNNIndependent variablesMultiple linear regressionsMunicipal watersPerformance criterionRadial basis neural networksRoot mean square errorsSocio-economicTraining and testingWater consumptionWater useNeural networksArticle10.1007/s11269-008-9291-3