This paper presents an adaptive neural fuzzy network model for seasonal streamflow forecasting. The model is based on a constructive learning method that adds neurons to the network structure whenever new knowledge is necessary so that it learns the fuzzy rules and membership functions essential for modeling a fuzzy system. The model was implemented to forecast monthly average inflow on an one-step-ahead basis. It was tested on three hydroelectric plants located in different river basins in Brazil. When the results were compared with those ofa multilayer feedforward neural network model, the present model revealed at least a 50% decrease in the forecasting error.