Multi-step-ahead monthly streamflow forecasting by a neurofuzzy network model

The analysis and forecasting of seasonal streamflow series are of utmost importance in the operation planning of water resource systems. One of the greatest difficulties in forecasting these series is the seasonal nature of streamflow series, due to wet and dry periods of the year. Moreover, real-world data are noisy and may contain contradictions and imperfections. Tolerance to imprecision and uncertainty is also required in order to achieve tractability and robustness. Fuzzy set-based data analysis models are especially suitable for these purposes. This suggests the application of neurofuzzy network models to seasonal streamflow forecasting. In this paper, a class of neurofuzzy network is applied to the problem of seasonal streamflow forecasting. This model is based on a constructive, competitive learning method where neuron groups compete when the network receives a new input, so that it learns the essential parameters to model a fuzzy system, which are the fuzzy rules and membership functions. A database of the average monthly inflows from a Brazilian hydroelectric plant was used. The performance of the model was compared with conventional approaches and the results show that the proposed model has a better performance than other methodologies which consider one-step-ahead forecasting and multi-step-ahead forecasting