An Improved Long-Term Water Level Prediction Method for N-BEATS

The water level of hydrological observatory has important reference significance in water resources utilization, drought and flood control and water supply and drainage. Aiming at the problem of poor accuracy and difficult training in long-term water level prediction, an improved time series model, WOA-N-Beats, is proposed, and the whale optimization algorithm is introduced to solve the problem of the model falling into local optimal solutions and the difficulty of finding optimal parameters. In the water level prediction of Wulong, Yichang, and Shashi stations in the next 512 days, the experiments show that compared with RNN and LSTM methods, WOA-N-Beats has the best MAE, MSE, and RMSE, and the enhancement effect is large, which realizes a more accurate prediction.