A Data Driven Method to Build Learning Models for Correlation Analysis of Regional Air Quality

With the consciousness of environmental protection is going to rising up globally, every government have seriously concerned the related issues of PM2.5. In essence, PM2.5 is the microscopic contaminated particles. The sizes of particles are just one of twenty-eighth of human's hair, in addition to penetrating the respiration system, carrying the heavy mental, dioxin and bacteria, through the blood circulation could intrude easily into the human alveoli. Furthermore, the contaminated particles may cause great damages to human health. Thus, in this work we investigate some factors which may influence the air quality related to PM2.5, including temperature, humidity, and wind speed. In particular, we collected the data of the factors in several cities in Taiwan for experimentation. After removing the outlier data samples in the database, we make use of the Support Vector Machines (SVM) and Support Vector Regression (SVR) techniques to experiment with the collected data for correlation analysis of regional air quality.