Artificial neural networks for streamflow prediction

This paper presents monthly streamflow prediction using artificial neural networks (ANN) on mountain watersheds. The procedure addresses the selection of input variables, the definition of model architecture and the strategy of the learning process. Results show that spring and summer monthly streamrlows can be adequately represented, improving the results of calculations obtained using other methods. Better streamflow prediction methods should have significant benefits for the optimal use of water resources for irrigation and hydroelectric energy generation.