DEVELOPMENT OF THE REAL-TIME RIVER STAGE PREDICTION METHOD USING DEEP LEARNING

Abstract

 The real-time river stage prediction model was developed using the artificial neural network model, with deep learning as the training method. The main component of the model was the four-layer feed-forward network. As a network training method, the stochastic gradient descent method based on the back propagation technique was applied. The denoising autoencoder was applied as a pre-training method. The developed model was applied to one catchment of the Ooyodo River, one of the first-grade rivers in Japan. The hourly change in river stage and hourly rainfall were used as input to the model, while output data was the river stage of Hiwatashi. To clarify the suitable configuration of the model, a case study was done. The prediction result was compared with those of other prediction models. Consequently, the developed model showed the best performance.