River water level forecast based on spatio-temporal series model and RBF neural network

River water level prediction is not only an important part of hydrological forecasting, but also a hot topic. It is a challenge to river water level prediction, for its level fluctuation, time and space variability, multidimensional, dynamic and uncertainty. Considering the temporal and spatial information of river water level, this paper proposes a method based on spatio-temporal series model and RBF neural network, then predicts river water level of Xiangjiaba Station with the method. Moreover, the obtained results are compared to other forecast method. The experimental results show that the forecast method based on spatio-temporal series model and RBF neural network has the excellent performance of higher prediction precision.