Runoff Forecasting Using Machine-Learning Methods: Case Study in the Middle Reaches of Xijiang River

Abstract

Runoff forecasting is useful for flood early warning and water resource management. In this study, backpropagation (BP) neural network, generalized regression neural network (GRNN), extreme learning machine (ELM), and wavelet neural network (WNN) models were employed, and a high-accuracy runoff forecasting model was developed at Wuzhou station in the middle reaches of Xijiang River. The GRNN model was selected as the optimal runoff forecasting model and was also used to predict the streamflow and water level by considering the flood propagation time. Results show that (1) the GRNN presents the best performance in the 7-day lead time of streamflow; (2) the WNN model shows the highest accuracy in the 7-day lead time of water level; (3) the GRNN model performs well in runoff forecasting by considering flood propagation time, increasing the Qualification Rate (QR) of mean streamflow and water level forecast to 98.36 and 82.74%, respectively, and illustrates scientifically of the peak underestimation in streamflow and water level. This research proposes a high-accuracy runoff forecasting model using machine learning, which would improve the early warning capabilities of floods and droughts, the results also lay an important foundation for the mid-long-term runoff forecasting.

Keywords: forecast; generalized regression neural network (GRNN); machine learning; streamflow; water level; wavelet neural network (WNN).