Study on Hydrology Time Series Prediction Based on Wavelet-neural Networks

To improve the performance of the model of wavelet-neural network for complex time series, a novel multi-factor prediction model is proposed, which inherits the advantage of previous wavelet-neural network and makes full use of the characteristics of different time series on different time scales. In addition, this paper also put forwards a criterion for the selection of wavelet functions, which is based on the weighted sum of the linear correlation coefficients of different time series with the target peer series. At last, the proposed model is applied to the prediction of daily discharge of WANGJIABA station at HUAIHE River and the experimental results show that higher accuracy is achieved, compared with previous models.