Various 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.

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In this study, the applicability of an adaptive neuro‐fuzzy inference system (ANFIS) to forecast for monthly river flows is investigated. For this, the Göksu river in the Seyhan catchment located in southern Turkey was chosen as a case study. The river flow forecasting models having various input structures are trained and tested by the ANFIS method. The results of ANFIS models for both training and testing are evaluated and the best‐fit forecasting model is determined. The best‐fit model is also trained and tested by feed forward neural networks (FFNN) and traditional autoregressive (AR) methods, and the performances of the models are compared. Moreover, ANFIS and FFNN models are verified by a validation data set including river flow data records during the time period 1997–2000. The results demonstrate that ANFIS can be applied successfully and provides high accuracy and reliability for monthly river flow forecasting.