基于小波神经网络的水库来水量预测模型

A Forecast Model of Reservoir Inflow Based on Wavelet Neural Network

水库来水量预测是水库调度和水资源优化配置的重要依据,所以其预测精度值得引起重视.通过对小波分析理论和神经网络理论的研究,将两者结合起来,运用小波神经理论方法对水库来水量进行预测.取实例进行建模分析,并建立BP模型与之比较分析,计算结果为小波神经网络预测模型的精度比BP高很多,可以用于来水量预测.

The forecast of water inflow of a reservoir is an important basis for reservoir operation and optimal allocation of water resources.Therefore,its prediction accuracy is worth attention.Based on the study of wavelet analysis and neural network theory,combining them together,this paper uses wavelet neural theory to predict reservoir inflow.This paper also takes an example to model and analysis,BP model is built to make compatative analysis.As a result,the accuracy of wavelet neural theory model is more higher than that of BP model;that is to say,the model can be used to predict reservoir inflow.