A Seasonal Streamflow Forecasting Model Using Neurofuzzy Network
A class of neurofuzzy networks and a constructive, competition - based learning procedure is presented as a vehicle to develop fuzzy models. Seasonal streamflow forecasting models are of particular interest here because it poses substantial modeling challenges, and it is of paramount importance when solving water resources systems operation planning problems. The network learns membership functions parameters for each input variable from training data, processes data following fuzzy reasoning principles, and has input space partition automatically adjusted to cover the whole input space. These are essential design issues when developing fuzzy models of systems and processes. The problem of seasonal streamflow forecasting is solved using a database of average monthly inflows of three Brazilian hydroelectric plants located at different river basins. Comparison of the neurofuzzy model with multilayer neural network and periodic autoregressive models are also included to illustrate the performance of the approach. The results show that the neurofuzzy model provides a better one-step-ahead streamflow forecasting, with forecasting errors significantly lower than the other approaches.
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