Previsão de vazões médias mensais usando redes neurais nebulosas

Abstract

This paper presents a neural fuzzy network model for seasonal streamflow forecasting. The model is based on a constructive learning method where neurons groups compete when the network receives a new input, so that it learns the fuzzy rules and membership functions essential for modelling a fuzzy system. The model was applied to the problem of seasonal streamflow forecasting using a database of average monthly inflows of three Brazilian hydroelectric plants located at different river basins. The performance of the model developed was compared with conventional approaches used to forecast streamflows. The results show that the neural fuzzy network model provides a better one-step-ahead streamflow forecasting, with forecasting errors significantly lower than the other approaches.