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Mappings of the stimuli effects and the input and output estimates of artificial neural networks (ANN) are obtained via combinations of nonlinear functions. This approach offers the advantages of self-learning, self-organization, self-adaptation, and fault tolerance as well as the potential for use in flood forecasting applications. Furthermore, the ANN technology allows the use of multiple variables in both the input and output layers. This capability is very important for flood calculation because the stage, discharge, and other hydrological variables often are functions of many influential variables. Herein, we propose a flood forecasting system with related application, based on ANN. This method offers better performance and efficiency. [ABSTRACT FROM AUTHOR]