The atmosphere ozone content forecast model was established based on the combination of wavelet decomposition and advanced Least Square Support Vector Machine (LSSVM) regression. This can be approached in three steps: (1)The observations were decomposed into several different frequency signal subsets,(2)the independent prediction models of decomposed signals with Takens delay embedding theorem and Least-Squares Support Vector Machine (LSSVM) were set up, (3)independent predicted results were integrated as the final prediction with wavelet reconstruction. Application experiments with data from Xianghe and the other three observation stations show that the method can make better prediction effectively for the atmospheric ozone content, as compared with conventional Support Vector Machine(SVM) and Artificial Neural Network(ANN).
朱佳,王振会,金天力,郝晓静.2010.基于小波分解和最小二乘支持向量机的大气臭氧含量时间序列预测[J].气候与环境研究,15(3):295-302. ZHU Jia, WANG Zhenhui, JIN Tianli, HAO Xiaojing.2010. Combination of Wavelet Decomposition and Least Square Support Vector Machine to Forcast Atmospheric Ozone Content Time Series[J]. Climatic and Environmental Research (in Chinese],15(3):295-302.复制