Dual-Stage Attention-Based LSTM Network for Multiple Time Steps Flood Forecasting

Abstract

Flood forecasting plays a crucial role in supporting decision-making for flood management. In addition to conceptual and physical-based models, the data-driven models have garnered increasing attention in recent years. The proposed model in this study employs LSTM networks, Encoder-Decoder framework, as well as feedback and attention mechanism to effectively utilize diverse observed data and future rainfall as inputs for multiple time steps flood forecasting. The accuracy and reliability of the model have been validated across case studies in multiple watersheds in China. The results demonstrate the high performance of the LSTM-based flood forecasting model. Meanwhile, the efficacy of both the feedback mechanism and attention mechanism has been validated in the domain of flood prediction.