The correct prediction of reservoirs water level variation is one of the important issues for designing, operation of dams, and water supply management. In this study, based on four soft models which are the support vector regression (SVR), adaptive neurofuzzy inference system (ANFIS), artificial neural network (ANN), radial basis function neural network (RBFNN), and combinatory use of their results as input to one of these four models, a new structure is proposed. It is named supervised intelligence committee machine (SICM) for monthly reservoir water level prediction of the Karaj Amirkabir dam. Evaluation of the above models is performed by nine error criteria and eventually the best model among them is selected by the vikor decision-making method. The supervised support vector regression (SICM-SVR) is shown high accurate in monthly prediction rather than SVR model with increasing the Nash-Sutcliffe efficiency (NS) from 0.58 to 0.81 (over 39% increase) and decreasing the mean square error (MSE) from 117.8 to 55.78 m2 (over 52% decrease). According to the vikor analysis among all soft and hybrid models, the SICM-ANN is selected as the best model with NS and MSE equal to 0.94 and 12.85 m2 , respectively. Generally, the proposed method results show that all supervised (hybrid) models have higher performance than soft ones and can be effectively applied to reduce the predicted error of water level.
