Renmin Zhujiang (Jun 2024)
Non-Stationary Extreme Runoff Prediction Model of Snowmelt Flood in Flood Season Based on VMD-NGO-LSTM and Its Application
Abstract
Jingou River is a typical snowmelt recharge basin. Due to the influence of natural environments, climate changes, and human activities, the extreme runoff sequence in flood season shows non-stationary and complex characteristics, which brings new challenges to the accurate prediction of extreme runoff of the basin in flood season. In order to eliminate the influence of the non-stationarity of extreme runoff in the flood season on the prediction results in the basin, the variational mode decomposition (VMD) algorithm was introduced, and a combined prediction model (VMD-NGO-LSTM) based on northern goshawk optimization (NGO) and long short-term memory neural network (LSTM) was proposed. It was applied to the extreme runoff prediction of the Bajiahu hydrological station in the Jingou River Basin from 1964 to 2016. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and Nash coefficient (NSE) were used to evaluate the prediction ability of the model. The results show that: ① According to the change in hydrological characteristics including period and trend of the extreme runoff sequence of the snowmelt flood in the Jingou River Basin in the flood season, the maximum runoff sequence and minimum runoff sequence are non-stationary. ② The NSE values of the VMD-NGO-LSTM prediction models are all greater than 0.97, and the RMSE, MAPE, and MAE values are all small. Compared with the VMD-LSTM model and VMD-NGO-BP model, the VMD-NGO-LSTM model can well predict the change process of extreme runoff of Bajiahu hydrological station in flood season. This study provides a new idea for predicting extreme runoff in flood season and has a certain reference value for flood control and disaster reduction in Xinjiang.
