【摘要】 针对大气PM_2.5质量浓度的非线性和非平稳性的特点,为了提高PM_2.5质量浓度的预测精度,采用"分解与整合"的预测方法,建立了基于互补集合经验模态分解（complementary ensemble empirical mode decomposition,CEEMD）和支持向量回归（support vector regression,SVR）的混合预测模型（CEEMD-SVR）.该模型首先采用CEEMD对PM_2.5质量浓度的原始时间序列进行分解,得到若干具有不同时间尺度的相对平稳分量;然后采用SVR算法对各个分量分别进行预测;最后求出各个分量的预测值之和,作为原始PM_2.5质量浓度的预测结果.选取北京市海淀区万柳监测站点2014年3月1日—2015年4月30日的PM_2.5日均质量浓度以及北京市怀柔监测站点2014年5月1日—2015年4月30日的PM_2.5日均质量浓度作为实验样本集.研究结果与EEMD-SVR、EMD-SVR和单一SVR模型进行对比,表明CEEMD-SVR模型有效提高了PM_2.5质量浓度的预测精度.更多还原

【Abstract】 To solve the problem of nonlinearity and instability of atmospheric PM_2.5 mass concentration and improve the prediction accuracy,a new hybrid method based on complementary ensemble empirical mode decomposition（ CEEMD） and support vector regression（ SVR） was established by using the method of "decomposition and integration". First,the original sequence of PM_2.5 mass concentration was decomposed by CEEMD,and several relatively stationary components with different time scales were obtained. Second,each component was predicted by SVR, respectively. Finally, the sum of the predicted values of each component was obtained as the prediction result of the original PM_2.5 mass concentration. The daily average PM_2.5 mass concentration from March 1,2014 to April 30,2015 in Wanliu monitoring site of Beijing and the daily average PM_2.5 mass concentration from May 1,2014 to April 30,2015 in Huairou monitoring site of Beijing were selected as experimental samples. Results showthat CEEMD-SVR model can effectively improve the prediction accuracy of PM_2.5 mass concentration when compared with the EEMD-SVR,EMD-SVR and single SVR models.更多还原