In order to improve the accuracy of streamflow forecast,the ERRIS model was improved based on LSTM,and the ERRIS-LSTM model was constructed for real-time correction of streamflow forecast.The Yarlung Zangbo River and Jiao River basins were taken as examples for comparative analysis.The results showed that,compared with the ERRIS model,the ERRIS-LSTM model increased the Nash-Sutcliffe efficiency coefficient by 4.1％and 1.1％,decreased the root mean squared error by 67.7％and 5.7％in streamflow forecast of the Yarlung Zangbo River and Jiao River basins,respectively.Especially for medium and low flows of the Yarlung Zangbo River Basin,the values of percent bias of streamflow forecast obtained by the ERRIS-LSTM model were reduced by 75.5％and 79.1％,respectively,and the statistical indexes of low flow in the Jiao River Basin obtained by the ERRIS-LSTM model were improved by more than 20％.The ERRIS-LSTM model could fully capture the continuity of the error series,and the ensemble forecasts generated by the ERRIS-LSTM model were more accurate,less uncertain,and more reliable than those of the ERRIS model,with the continuous ranked probability score reduced by more than 75％.In comparison with the deterministic corrected results of the LSTM model,the ERRIS-LSTM model can provide additional uncertainty information,which is promising in operational forecasting and decision-making in flood control.
关键词
径流预报/实时校正/深度学习/ERRIS模型/LSTM模型/雅鲁藏布江流域/椒江流域
Key words
streamflow forecast/real-time correction/deep learning/ERRIS model/LSTM model/Yarlung Zangbo River Basin/Jiao River Basin
维普
万方数据