基于小波分解与GA-LSSVM的GPS可降水量短临预报

针对GPS可降水量时间序列具有非线性、非平稳性的特征，研究一种基于小波分解（WD）、遗传算法（GA）和最小二乘支持向量机（LSSVM）的GPS可降水量短临预报方法。先采用小波分解将GPS可降水量时间序列分解成便于预报的低频分量和高频分量；然后利用遗传算法优化LSSVM参数，进而对各分量建立预报模型；再将各分量预报结果进行叠加重构得到最终预报结果。选取两组数据进行实验，并将预报结果分别与LSSVM和遗传小波神经网络（GA-WNN）预报结果进行对比。结果表明，该组合模型具有良好的泛化能力，可有效解决神经网络易陷于局部极小的问题，提高了全局预报精度。

Short-Impending Prediction of GPS Precipitable Water Vapor Based on Wavelet Decomposition and GA-LSSVM

Aiming at the random and nonlinear characteristic of the time series of GPS precipitable water vapor(PWV), this paper proposes a new short-impending prediction method of GPS PWV based on wavelet decomposition(WD), genetic algorithm(GA) and least squares support vector machine(LSSVM). First, WD is used to decompose the GPS PWV time series into low frequency and high frequency components, which are easy to predict. Second, GA is used to optimize the parameters of LSSVM, and the prediction model of each component is established. Finally, the results of each component prediction are superimposed and reconstructed to get the final prediction results. In this paper, two groups of data are selected for experiments, and the prediction results are compared with those of LSSVM and genetic wavelet neural network(GA-WNN). The results show that the combined model has good generalization ability, can effectively solve the problem that neural network tends to trap in local minimum, and improves global prediction accuracy.