In view of the high nonlinearity of runoff time-series process, we establish a Wavelet-PSO-Support vector regression (WPSO-SVR) coupled model to predict daily runoff by combining wavelet analysis, particle swarm optimization with support vector regression.The model can fully exploit the multi-resolution function of wavelet analysis and the nonlinear approximation ability of support vector regression, and decompose the daily runoff time series into different frequency sub-sequences with Wavelet analysis.Then we use particle swarm global search function to find the optimal model parameters and construct a model with these reconstructed sequences as inputs, and apply the model to daily runoff prediction at Shigu hydrologic station in the middle reaches of Jinsha River.The results show that the predictive effect of the WPSO-SVR coupled model is better than the single Support vector regression model and it has strong adaptability in daily runoff forecasting.%针对径流时间序列过程的高度非线性,将小波分析方法、粒子群优化算法与支持向量回归相结合,建立了小波-粒子群-支持向量回归耦合日径流预测模型(WPSO-SVR).该模型充分挖掘小波分析的多分辨功能和支持向量回归的非线性逼近能力,应用小波分析方法将日径流时间序列分解为不同频段的子序列,将重构后的序列作为模型的输入,利用粒子群全局搜索能力实现模型参数寻优,得到最佳模型参数,构建模型,并将该模型应用于金沙江中游石鼓站日径流预测.结果表明,该模型的预测效果明显优于单一支持向量回归模型,在日径流预测中具有较强的适应性.
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