Abstract: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.
XIE Shaofeng,SU Yongning,LIU Chunli et al. Short-Impending Prediction of GPS Precipitable Water Vapor Based on Wavelet Decomposition and GA-LSSVM[J]. jgg, 2019, 39(5): 487-491.