Applications of SVR-PSO Model and Multivariate Linear Regression Model in PM2.5 Concentration Forecasting

2. The Data Sources And Processing

In this paper, the data are collected from the Weather Report and the PM2.5 Data Network, in which five cities of Henan Province, China, are selected. These five selected cities include Zheng Zhou City (the provincial capital of Henan Province), An Yang City, Luo Yang City, Ping Ding Shan City, and Jiao Zuo City; in which, the previous two cities are the industrial cities and the other three cities are belonged to the resource-based cities (Zhang et al., 2015b). The data contents mainly contain PM2.5 concentration and the dependent variable AQI index. The period of the collected data is from 23 April 2015 to 31 July 2015; 24 May 2016 to 31 August 31 2016; and 23 June 2017 to 30 September 30 2017; totally 300 days in the collected data set. In which, 200 days are used as the training set to establish the training model, the other 100 days are used to establish the testing model (Yang et al., 2013). In the training stage, the parameters of an SVR model would be determined with minimal forecasting errors, and the un-used raw data will be employed to calculate the forecasting errors. The testing results has demonstrated that the proposed approach receives more accurate forecasting performances, which could provide valuable and meaningful governance guidance than the simple linear regression model (Lv et al., 2016).