Effective improvement of multi-step-ahead flood forecasting accuracy through encoder-decoder with an exogenous input structure

Abstract

Accurate and reliable multi-step-ahead flood forecasting is beneficial for reservoir operation and water resources management. The Encoder-Decoder (ED) that can tackle sequence-to-sequence problems is suitable for multi-step-ahead flood forecasting. This study proposes a novel ED with an exogenous input (EDE) structure for multi-step-ahead flood forecasting. The exogenous input can be the outputs of process-based hydrological models. This study constructs four multi-step-ahead flood forecasting approaches, including the Xinanjiang (XAJ) hydrological model, the single-output long short-term memory (LSTM) neural network with recursive strategies, the recursive ED combined with the LSTM neural network (LSTM-RED), and the LSTM-EDE models. The performance of these four models is evaluated and compared by the long-term 3 h hydrologic data series of the Lushui and Jianxi basins in China. The results show that the LSTM-RED model that integrates recursive strategies into the training process of neural networks is more advantageous than the LSTM model. The proposed LSTM-EDE model can overcome the exposure bias problem, simplify its model structure, increase the computational efficiency in the validation process, and improve the multi-step-ahead flood forecasting accuracy, as compared to the LSTM-RED model.