PM2.5 prediction based on modified whale optimization algorithm and support vector regression
Zuhan Liu, Xin Huang, Xing Wang
Abstract
In order to obtain the pattern of variation of PM 2.5 concentrations in the atmosphere in Nanchang City, we build a Support Vector Regression(SVR) with modified Whale Optimization Algorithm(WOA) hybrid model (namely mWOA-SVR model) that can predict the PM 2.5 concentration. Firstly, according to the Pearson correlation coefficient (PCC) method to examine the dynamic relationship between air pollutants and meteorological factors together with them, PM 10 , SO 2 and CO were selected as air pollutant concentration characteristics, while daily maximum and minimum temperatures, and wind power levels were selected as meteorological characteristics; then, using modified WOA algorithm for parameter selection of SVR model, four sets of better parameter combinations were found; finally, the mWOA-SVR model was built by the four sets parameters to predict PM 2.5 concentration. The results show that the prediction accuracy of mixed mWOA-SVR model with pollutant concentration plus weather factors as the feature was higher than single pollutant concentration.