A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting

Abstract

Abstract The conceptual hydrologic model has been widely used for flood forecasting, while long short-term memory (LSTM) neural network has been demonstrated a powerful ability to tackle time-series predictions. This study proposed a novel hybrid model by combining the Xinanjiang (XAJ) conceptual model and LSTM model (XAJ-LSTM) to achieve precise multi-step-ahead flood forecasts. The hybrid model takes flood forecasts of the XAJ model as the input variables of the LSTM model to enhance the physical mechanism of hydrological modeling. Using the XAJ and the LSTM models as benchmark models for comparison purposes, the hybrid model was applied to the Lushui reservoir catchment in China. The results demonstrated that three models could offer reasonable multi-step-ahead flood forecasts and the XAJ-LSTM model not only could effectively simulate the long-term dependence between precipitation and flood datasets, but also could create more accurate forecasts than the XAJ and the LSTM models. The hybrid model maintained similar forecast performance after feeding with simulated flood values of the XAJ model during horizons to . The study concludes that the XAJ-LSTM model that integrates the conceptual model and machine learning can raise the accuracy of multi-step-ahead flood forecasts while improving the interpretability of data-driven model internals.