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.
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.
出处
《系统工程理论与实践》
EI
CSCD
北大核心
2009年第1期152-158,共7页
Systems Engineering-Theory & Practice
基金
国家自然科学基金(50879051)
