Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River

Abstract

The most important motivation for streamflow forecasts is flood prediction and longtime continuous prediction in hydrological research. As for many traditional statistical models, forecasting flood peak discharge is nearly impossible. They can only get acceptable results in normal year. On the other hand, the numerical methods including physics mechanisms and rainfall-atmospherics could provide a better performance when floods coming, but the minima prediction period of them is about one month ahead, which is too short to be used in hydrological application. In this study, a deep neural network was employed to predict the streamflow of the Hankou Hydrological Station on the Yangtze River. This method combined the Empirical Mode Decomposition (EMD) algorithm and Encoder Decoder Long Short-Term Memory (En-De-LSTM) architecture. Owing to the hydrological series prediction problem usually contains several different frequency components, which will affect the precision of the longtime prediction. The EMD technique could read and decomposes the original data into several different frequency components. It will help the model to make longtime predictions more efficiently. The LSTM based En-De-LSTM neural network could make the forecasting closer to the observed in peak flow value through reading, training, remembering the valuable information and forgetting the useless data. Monthly streamflow data (from January 1952 to December 2008) from Hankou Hydrological Station on the Yangtze River was selected to train the model, and predictions were made in two years with catastrophic flood events and ten years rolling forecast. Furthermore, the Root Mean Square Error (RMSE), Coefficient of Determination (R2), Willmott's Index of agreement (WI) and the Legates-McCabe's Index (LMI) were used to evaluate the goodness-of-fit and performance of this model. The results showed the reliability of this method in catastrophic flood years and longtime continuous rolling forecasting.