基于小波神经网络的水文时间序列预测

复杂时间序列预测是时间序列分析的主要研究内容之一,已成为一个具有重要理论和实际应用价值的热点研究领域。基于小波和神经网络组合模型,提出一种多因子小波预测模型以提高水文时间序列的预测精度。并根据不同小波函数对水文时间序列数据的适应性,提出了一种基于加权相关系数的小波函数选择准则。以国家重要水文站淮河王家坝站汛期的日流量时间序列预测为例,对各种常用小波函数进行了实验。结果发现选择得到的Haar小波和B3 spline小波函数预测精度较高,从而验证了小波函数选取准则的有效性;通过和传统单序列小波神经网络模型比较,发现提出的多因子小波神经网络模型的预测合格率在不同预见期均提高了10％以上,并且对洪水高流量方向预测合格率提高了15％。

Wavelet-neural network model based complex hydrological time series prediction

Time series prediction is one of the main research topics in time series analysis, which is of great importance both in theoretical and application aspects. To improve the performance of the wavelet-neural network model on complex time series, a novel multi-factor prediction model is proposed. According to the adaptability of different wavelet function to hydrological time series, a new criterion for the selection of different wavelet functions is also put forward, which is based on weighted correlation coefficients. Lastly, the newly proposed method has been tested on predicting the daily flow of WANGJIABA station, which is a very important observation site on HUAIHE river. It is found that the chosen Haar wavelet and B3 spline wavelet can produce higher prediction accuracy, which validates the effectiveness of the selecting principle of wavelet function. By comparing with traditional wavelet neural network for single time series, at least 10％ improvement has been observed for different predicting periods, and 15％ improvement in forecasting the high flow direction during the disastrous flood period. All the experimental results have shown that the proposed multi-factor prediction model is effective for complex hydrological time series prediction.