Applicability of the deep learning flood forecast model against the flood exceeding the training events

Abstract

Although artificial neural networks (ANN) is widely used for real-time flood prediction model, it is pointed out that the weak point of the model is poor applicability for the inexperienced magnitude of flood. In this study, the ANN models were applied to Abashiri River catchment. The training period of the ANN models were 1998-2015. The validation data was the 2016's largest flood since the river-stage observation had started. The main component of the model was the four-layer feed-forward network. As a network training method, the deep learning based on the denoising autoencoder was applied. The river-stage prediction up to 6 hours showed very good accuracy, and proved it can nicely predict the such a large flood.