人民长江2015，Vol.46Issue(24)：24-28,5.DOI:10.16232/j.cnki.1001-4179.2015.24.007
不同组合小波神经网络模型对径流预测的适用性
Applicability of different combination of wavelet neural network models to runoff prediction
摘要
Abstract
According to the non -stationary characteristics of hydrological time series, taking the average annual runoff at Yichang Station from 1904 to 2003 for example, two coupling forecasting models, wavelet analysis ( WA) and the BP neural net-work, wavelet analysis ( WA) and radial basis function ( RBF) neural network, are established, and the forecasting results are compared with the traditional single artificial neural network model, also the prediction effects are analyzed by five frequently used prediction performance evaluation index. The results show that the prediction accuracy of the integrated model is higher than that of the single model;the RBF network model is superior to the BP network model both in the integrated model and single model;WARBF integrated model has better prediction accuracy and generalization ability, which is a useful way to increase prediction accuracy and is feasible for runoff prediction.关键词
ATrous小波分析/BP神经网络/径向基函数神经网络/预测模型/水文预报Key words
A Trous wavelet analysis/Back-Propagation neural network/radial basis function neural network/prediction model/hydrological forecast分类
天文与地球科学引用本文复制引用
彭欣怡,于国荣,张代青..不同组合小波神经网络模型对径流预测的适用性[J].人民长江,2015,46(24):24-28,5.基金项目
国家地区科学基金(51469009,51269006) （51469009,51269006）
