摘要
利用小波分析识别年径流周期得到年径流周期成分,再对剔除周期成分的剩余序列进行小波消噪,消噪后的序列作为平稳随机成分建立自回归模型,并把噪声序列作为独立随机成分进行模拟,最后把周期成分、相依随机成分和独立随机成分组合建立随机模拟模型。实例研究表明,基于小波分析的水文随机模型比传统随机模型的模拟效果好,统计参数更接近实测序列的统计参数。
The yearly periodic components in hydrologic runoff series are obtained by wavelet analysis. The rest series, which has been eliminated the period components, is de-noised by wavelet to obtain the dependent stochastic components. And the noise series are taken as the independent stochastic components to be simulated. Then autoregression model is established with the dependent stochastic series. Finally, the periodic components, the correlative stochastic components and the independent components are combined to build stochastic model. Comparing with traditional stochastic methods, the example results show that the calculated parameters of hydrological series simulated with wavelet method are more close to those of measured series.
出处
《水电能源科学》
2007年第6期1-3,40,共4页
Water Resources and Power
基金
国家自然科学基金资助项目(50679053)
关键词
水文时间序列
随机模型
小波分析
周期
随机成分
hydrological time series
stochastic model
wavelet analysis
period
stochastic components
