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

• Published in: Ocean engineering Vol. 302; p. 117567
• Main Authors: Deng, Yangyu, Zhang, Di, Cao, Ze, Liu, Yakun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.06.2024
• Subjects: Artificial intelligence; Dam break flow; Deep learning; Spatio-temporal prediction; Water height prediction; Deep learning; Dam break flow; Water height prediction; Artificial intelligence; Spatio-temporal prediction
• ISSN: 0029-8018; 1873-5258
• DOI: 10.1016/j.oceaneng.2024.117567

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. [Display omitted] •A novel deep learning model named AE-LSTM-ATT is proposed.•AE-LSTM-ATT is used to achieve the spatial-temporal water height prediction.•AE performs better than PCA and KPCA.•LSTM-ATT outperforms RNN and LSTM.•AE-LSTM-ATT can be applied in real-time decision making for dam-break floods.