HYBRID DEEP NEURAL NETWORK AND DISTRIBUTED RAINFALL-RUNOFF MODEL FOR REAL-TIME RIVER-STAGE PREDICTION

Abstract

 We developed a real-time river stage prediction model using a hybrid deep neural network and physically based distributed rainfall-runoff model. The main component of the hybrid model was a four-layer feed-forward artificial neural network. Using the predicted flow of the rainfall-runoff model as the input data of the neural network, we integrated the two models into the hybrid model. The input data of the hybrid model included upstream water level, hourly change in water level, and estimated hourly change in catchment storage. The output was the change in water level at the prediction point. In the training phase, input data and supervised data were formed using the observed data. In the prediction phase, input data were formed using a combination of the observed data and flowrate calculated using the distributed mode.

 The result of the hybrid model outperformed those of the ANN and distributed models. Especially in the largest flood event, the performance of the hybrid model was significantly stronger.