Spatio-temporal water height prediction for dam break flows using deep learning

Abstract

Spatio-temporal prediction of the water height for dam break flow is of great significance in flood control projects. In this article, we propose a novel deep learning model named AE-LSTM-ATT that combines AutoEncoder (AE) and Long Short-Term Memory with Attention (LSTM-ATT) to predict the dynamic behavior of the dam-break water height field in spatial and temporal dimensions. The prediction performance of the AE-LSTM-ATT model was demonstrated using dam-break flow cases with different initial heights of the water column. The AE first compresses high-dimensional inputs into low-dimensional latent space using its encoder to enable a fast computation process. Then, the latent space is adopted as an input for the LSTM-ATT to predict low-dimensional representations at future time instances that can be restored to the water height field by the decoder of the AE. The major findings of this article include three parts: (1) AE-LSTM-ATT model can successfully achieve the spatio-temporal prediction of the water height for dam break flows with high precision; (2) The proposed AE presents much better performance than those of the widely used Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) in the dimension reduction; (3) The developed LSTM-ATT performs far better than the commonly used Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) in the time-series prediction. These promising results suggest that the proposed AE-LSTM-ATT model can play an essential role in capturing and advancing the spatio-temporal features of dam-break water height, which can be applied in the field of real-time decision making for dam-break floods.