ARIMA-SVM combination prediction of PM2.5 concentration in Shenyang
SONG Guo-jun1, GUO Xiao-dan1, YANG Xiao1, LIU Shuai2
1. School of Environment, Renmin University of China, Beijing 100872, China;
2. Agricultural Management Institute of the Ministry of Agriculture and Rural Affairs, Beijing 102208, China
Firstly, meteorological types of heating period and non-heating period were classified using the method of regression tree classification, and meteorological types which are likely to cause severe pollution were identified. Secondly, the daily mean value prediction model of PM2.5 concentration of different meteorological types was established using the combination of Autoregressive Integrated Moving Average Model and Support Vector Machine (ARIMA+SVM), which takes the emission of pollution sources as independent variables. In this paper daily mean PM2.5 concentration of 9environmental monitoring points with continuous data in Shenyang during Jan 2013 to June 2017 was analysed. The results show that, compared with ordinary machine learning model without weather classification, the prediction of daily mean PM2.5 concentration using ARIMA+SVM combined model based on meteorological classification has a better agreement with actual value, and its ability to identify the peak and valley values is much stronger. In heating and non-heating period, this combined model has the advantages of lower average error and higher prediction accuracy.
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