Abstract
AbstractThe 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.
Highlights
Accurate multi-step-ahead flood forecasting has important guiding significance for reservoir operation, flood control, and water resources management (Noori & Kalin 2016; Zhang et al 2016; Young et al 2017) while it has been a challenge all the time, subject to a dynamic climate environment and complex hydrological process (Xie et al 2019; Zhou et al 2020)
This study proposes a hybrid model that integrates the XAJ conceptual model and the long short-term memory (LSTM) neural network to make multi-step-ahead flood forecasts with considering the forecasting precipitation
The uncertainties from actual forecasting precipitation have great effects on flood forecasting and lead to a large deviation in the performance between the simulation and the actual application, the hybrid model can effectively handle the noise of forecasting precipitation via the LSTM module and improve multi-step-ahead forecasting performance of benchmark models in terms of Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), R, relative error (RE), and root mean square error (RMSE) indexes
Summary
Accurate multi-step-ahead flood forecasting has important guiding significance for reservoir operation, flood control, and water resources management (Noori & Kalin 2016; Zhang et al 2016; Young et al 2017) while it has been a challenge all the time, subject to a dynamic climate environment and complex hydrological process (Xie et al 2019; Zhou et al 2020). The single-output LSTM neural network has been found to conduct satisfying hydrographs and handle potential noise in the series (Nourani et al 2014; Zhang et al 2016; Hu et al 2018; Kratzert et al 2018) It cannot consider the physical mechanism, like other ANNs, and produces less accurate forecasting results in the long lead times due to the influence of available input variables (Kurian et al 2020). The XAJ model provides accurate flow simulation for the input of the hybrid model, which can significantly improve the performance of the LSTM neural network and effectively extend the lead time. The XAJ model and the LSTM neural network are constructed for comparison purposes
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