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基于小波消噪的混沌径流预测模型

期刊名称：华中科技大学学报（自然科学版）．37（7）：86-89，2009.7．（EI：20093812327
时间：0
分类：TV124[水利工程—水文学及水资源]
作者机构：[1]华中科技大学水电与数字化工程学院,湖北武汉430074
相关基金：国家重点基础研究发展计划资助项目（2007CB714107）;水利部公益性行业科研专项经费资助项目（200701008）;国家自然科学基金雅砻江联合研究基金重点项目（50539140）.
相关项目：市场条件下流域梯级水电能源联合优化运行和管理的先进理论与方法

关键词：径流预测, 混沌, 径向基函数, 相空间重构, 小波消噪, runoff prediction, chaos, radial basis function, phase-space reconstruction, wavelet denoising

中文摘要：

考虑到噪声对径流分析的影响以及传统消噪技术在径流消噪过程中存在的不足,引入小波变换技术对含噪信号进行分解和重构,通过对小波分解后的小波系数限定阈值来消除噪声.在此基础上,以相空间重构理论为依据,探讨了混沌径向基函数网络径流预测模型的建模思路、特点及关键参数的选取.最后以宜昌水文站为例,对其月径流时间序列进行了混沌识别和预测.实例表明：该模型能较好地处理复杂的径流序列,具有较高的泛化能力和很好的预测精度.

英文摘要：

The influence of noise and the disadvantages of traditional noise eliminating technologies erenow were considered. Wavelet theory was used to reduce the noise in runoff time series, and wavelet-transform technique was used to decompose and reconstruct noise signal, and by limiting the analyzed wavelet-coefficient to a threshold to eliminate the noise. On this basis, the prediction model of chaos time series was built by using the radial basis function network, which was based on the phasespace reconstruction theory, and we also discussed the building method, the characteristics of the model, as well as the selecting method for the key parameters. At last, taking the Yichang station of Yangtze River as an example, the proposed model was used to recognize the chaotic feature of the monthly runoff and make prediction, and the result verifies that this model can process a complex hydrological data series better,and is of better generalization ability and higher prediction accuracy.