RESEARCH ON PM2.5 CONCENTRATION COMBINATION FORECASTING MODEL BASED ON COR-SVM

• Main Authors: X. Y. Feng, P. Tian, Y. J. Shi, M. Zhang
• Format: Article
• Language: English
• Published: Copernicus Publications 2019-10-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W9/23/2019/isprs-archives-XLII-3-W9-23-2019.pdf

PM2.5 is a pollutant that can enter the lungs, threatening human health and affecting people’s living and traveling. In this paper, we use multivariate linear regression, support vector machine and their combined prediction method to predict the concentration of PM2.5. It is significant for the convenience of healthy life. This paper is based on a series of meteorological data such as O₃ concentration, CO concentration, SO₂ concentration, PM2.5 concentration and PM10 concentration from 2014 to 2018 in Beijing. By calculating the correlation coefficient between the concentration of PM2.5 and the concentration of the other four components, the multivariate linear regression equation was fitted by using the correlation coefficient with high correlation as the factor of multiple linear regression. Then we use support vector machine regression prediction method to predict the concentration of PM2.5. The combined prediction method is obtained by weighing the two prediction results. It is found that the prediction method of support vector machine is better in dealing with large-scale and small sample data prediction, and the multi-linear fitting method is better in processing short-term prediction. The combined prediction results based on correlation coefficients combine the advantages of the two prediction methods, and the prediction results are more reasonable.