Prediction method of runoff and sediment processes based on RNN and its application:a case study of the Lower Yellow River
Accurate and fast prediction of runoff and sediment processes is crucial for the efficient formulation of flood control schemes.A recurrent neural network(RNN)algorithm was selected in this study,which can memorize timing information.The study applied the RNN algorithm to the braided reach of the Lower Yellow River.Data of different years were classified based on the flow and sediment from the entrance station named Huayuankou Station.According to the classification result,respective RNN runoff-sediment processes prediction models suitable for different types of inflows of water and sediment to predict the runoff and sediment processes of Gaocun Station.After the model training was completed,inputting the runoff and sediment data at Huayuankou Station could output the predicted runoff and sediment data at Gaocun Station in the corresponding period.The prediction results show that:1)Pre-processing the datasets by classifying the data according to incoming runoff and sediment types can improve the prediction accuracy.Compared with the non-classification method,the prediction accuracy of flood peak and sediment peak may be improved by more than 50％.2)The predicted runoff and sediment sequences are in good agreement with the measured data,and the optimal Nash-Sutcliffe efficiency coefficient(NSE)can reach 0.99 and 0.92,respectively.The prediction effect of the annual flow process in flood season and non-flood season is similar,with NSEs around 0.97.In contrast,the prediction accuracy of sediment content process is better during the flood season(with the NSE around 0.88),as compared with that during the non-flood season.3)The prediction accuracy of the RNN model for the runoff process can reach or even exceed the Muskingen method,and can make up for the deficiency of the Muskingen method in predicting sediment process.Overall,the RNN runoff-sediment prediction method exhibits high accuracy,and it is suitable for predicting different types of runoff and sediment processes in the braided reach of the Lower Yellow River.
runoff and sediment process predictionmachine learningrecurrent neural networkbraided reachLower Yellow River
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