关键词: 水位预测; 多变量模型; 循环神经网络; 自回归模型; ARIMA; SVR; BP神经网络;

Abstract: There are many factors that affect the water level of a river. In view of the fact that traditional univariate hydrological prediction model cannot fully consider those factors, a multivariable water level prediction model based on AR-RNN was proposed. The model contained two parts, i.e., RNN and AR. RNN can introduces a large number of non-linear layers to help the model fit the non-linear components in the hydrological sequence. However, more non-linear layers reduces the sensitivity of model to linear components. AR can increase the model sensitivity to linear components, which can make the model's prediction at the peak of the water level to be more accurate. We applied the AR-RNN model to the water level prediction of the Qingxi River in Sichuan Province. The results showed that the AR-RNN model has higher prediction accuracy than the ARIMA model, SVR model and BP neural network

Key words: water level prediction; multivariable model; recurrent neural network; auto-regressive model; ARIMA; SVR; BP neural network;