基于多尺度分析与神经网络的需水量预测

采用小波多尺度分解的方法，将需水量时间序列分解为多个较平稳的细节子序列和一个趋势序列，再利用BP神经网络对分解后的各序列进行预测，把预测后的序列聚合重构，得到预测结果。以新疆石河子地区的需水量为例对该方法作了验证。表明多尺度分析与神经网络耦合预测，比单一BP神经网络预测精度更高，可满足实际需要。

Water demand forecasting based on multi-scales analysis and neural network

The time series of water demand can be decomposed into several stationary detailed time series and a tendency time series according to the algorithm of this multi-scales in this paper.Decomposed time series are forecasted with BP neural network to obtain the prediction series.Then the forecasting results are reconstructed by wavelet theory.So,the forecasting result is gained.An example of water demand of Shihezi in Xinjiang Province is used to testify the feasibility of the new method.The results show that the method of coupling multi-scale decomposition and BP neural network has advantages over the traditional BP neural network in predicted qualification-rate,and has feasibility in forecasting of time series.