【摘要】 针对小波变换方法的不足,运用EMD方法对黄河兰州以上二级水资源分区45年(1956- 2000年)的年降雨量序列进行多时间尺度分析,发现该区域年降雨量存在准3年、准4-8年、准11年波动周期,并探讨了各IMF分量的物理背景及其趋势变化;然后以年降雨量的EMD分量为输入,以相应的年径流量为输出,建立了基于EMD的年降雨径流BP神经网络预测模型.研究结果表明:EMD作为一种全新的信号处理方法,可以对水文时序进行精确的多时间尺度分析,进而掌握其局部变化规律,为人工神经网络提供高质量、多层次的输入变量,显著提高模型质量.更多还原

【Abstract】 Through analyzing problem of wavelet analysis,the annual precipitation series from 1956 to 2000 in the sub-water resources region of upper Lanzhou is decomposed into muti-time scale series with EMD method.The results show that the precipitation series has periods that about 3,4-8,11 years,and the physical backgrounds and trends of the IMF sub-series are discussed.An annual precipitation-runoff forecasting ANN model based on EMD is established.with the EMD decomposition series as input and the corresponding annual runoff as output.The study shows that,as a new and original signal decomposition method.Empirical Mode Decomposition namely EMD can be used as a tool to decompose hydrological time series into exact muti-time scale sub-series for finding their local change rule,and then to supply input variables with high quality and muti-level to enhance model quality.更多还原