Adaptive Neural Networks for Flood Routing in River Systems

Abstract

A methodology based on adaptive ANN models is proposed for flood routing in river systems. The proposed methodology is capable of modeling both converging and diverging river networks. A Multilayer Perceptron Network (MLP), a Recurrent Neural Network (RNN), a Time Delay Neural Network (TDNN) and a Time Delay Recurrent Neural Network (TDRNN) are applied in this study. An Adaptive training procedure based on the Forgetting Factor (FF) approach is used to train ANNs models. The methodology provides a lead time equal to travel time for the flood estimation downstream of the river. The performances of the models are tested within the two distinctive parts of the Karoon River in south-west Iran. The first case study uses synthetic floods generated by the HEC-RAS hydraulic model; the second one uses observed floods. Besides, the Muskingum routing method is used in the second case study to be compared with the results of ANN models. Overall, the results demonstrated that the proposed methodology performs well considering goodness-of-fit criteria. Moreover, the dynamic neural networks outperform the static MLP and the Muskingum model.