The practical research on flood forecasting based on artificial neural networks

The technologies of artificial neural networks can be used to complete information processing of the networks through the interaction of neural cells. The mappings of the stimuli effects and the input and output estimates are obtained via combinations of nonlinear functions. This offers the advantages of self-learning, self-organization, self-adaptation and fault tolerance. It also has the possibility of use in applications for flood forecasting. Furthermore, the ANN technology allows us multiple variables in both the input and output layers. This is very important for flood calculation since the stage, discharge, and other hydrological variables are often functions of many influential variables, which form the novelty value of the paper. For this research, the authors proposed a new flood forecasting system with related applications, based on the neural networks method. This method has been shown to offer better results in performance and efficiency. It is expected that the application of this system will increase sensitivity and further increase flood forecasting performance.