基于GR4J-LSTM混合模型的洪水预报研究

Research on flood forecasting based on GR4J-LSTM hybrid model

为提高洪水过程预报的准确性,将概念性水文模型GR4J (modèle du Génie Ruralà4 paramètres Journalier)的预报流量耦合到长短时记忆神经网络(Long Short-Term Memory Neural Network, LSTM)中,构建了GR4J-LSTM混合模型,并与GR4J、LSTM模型进行对比。基于2012~2019年陆水水库汛期与洪水事件相关的数据集,并结合欧洲中期天气预报中心的3 h预报降水产品,驱动GR4J-LSTM混合模型,预报陆水水库3~12 h预见期的入库流量。最后采用平均影响值(Mean Impact Value, MIV)算法评估输入变量的相对重要性。结果表明:GR4J、LSTM和GR4J-LSTM模型均具有较好的模拟预报能力,但GR4J-LSTM混合模型的预报性能最优,既可以学习GR4J模型的产汇流过程,又提高了洪水预报的精度。研究成果可为洪水预报方案制定提供参考。

To improve the forecasting accuracy of flood process, we coupled the forecasted inflow by the GR4J model(modèle du Génie Rural 1 4 paramètres Journalier)into the Long Short-Term Memory Neural Network(LSTM),to construct a GR4J-LSTM hybrid model, and compared it with GR4J and LSTM models.Based on the data set related to flood events in the flood season of Lushui Reservoir from 2012 to 2019,combined with the 3h precipitation forecast products from the European Centre for Medium-Range Weather Forecasts(ECMWF),the GR4J-LSTM hybrid model was driven to forecast the inflow of Lushui Reservoir during the forecast period of 3 to 12 hours, and finally the relative importance of the input variables was evaluated by the Mean Impact Value(MIV)algorithm.The results show that the GR4J,LSTM and GR4J-LSTM models can simulate and forecast inflow well and the GR4J-LSTM hybrid model performs best, not only can learn the runoff process of the GR4J model, but also improve the flood forecasting accuracy.The research results can provide a reference for the formulation of flood forecasting schemes.