水利水电技术2013，Vol.44Issue(2)：5-8,4.
基于小波分析—模糊神经网络的径流预报模型
Wavelet analysis-fuzzy neural network based runoff forecasting model
摘要
Abstract
In accordance with the wavelet decomposition and reconstruction technology as well as the training cycles of various fuzz-y neural network models, four runoff forecast models based on the combination of wavelet analysis with fuzzy neural network, i.e.Mallat algorithm based long cycle runoff forecasting model; Mallat algorithm based short cycle runoff forecasting model; wavelet packet algorithm based long runoff forecasting model; wavelet packet algorithm based short cycle runoff forecasting model, are put forward herein, and then the principle, structure and step of the establishment of the models are expatiated as well.Moreover, by taking the monthly runoff data from Tangnaihai Hydro-logical Station--one of the outlet hydrologjcal stations at the source regions of the Yellow River as the applied case, the four models mentioned above are comparatively evaluated with the cycle decomposition coefficient and Nash-Sutcliffe efficiency coefficient.The result shows that the forecasting effect is best from the Mallat algorithm based long cycle runoff forecasting model and that from the wavelet packet algorithm based short cycle runoff forecasting model is worst Thereby, the main causation of this phenomenon is also analyzed herein.Furthermore, some reasonable suggestions on the application of both the wavelet analysis and the fuzzy neural network to hydrological model are presented as well.关键词
小波分析/WANFIS/周期分解系数/径流预报模型Key words
wavelet analysis/ wavelet-based adaptive neuro-fuzzy inference system/ cycle decomposition coefficient/ runoff forecasting model分类
天文与地球科学引用本文复制引用
杜富慧..基于小波分析—模糊神经网络的径流预报模型[J].水利水电技术,2013,44(2):5-8,4.基金项目
国家"973"项目"气候变化对黄淮海地区水循环的影响机理和水安全评估"(2010CB951100) （2010CB951100）
教育部长江学者与创新团队"大气-陆面-水文过程耦合机理研究"(IRT0717) （IRT0717）
水利部公益性行业科研专项"中国极端洪水干旱预警与风险管理关键技术"(200801027). （200801027）
