Regional PM2.5 concentration prediction method of PSO-SVR model with weighting factors
This paper developed a regional air PM2.5 concentration predicting model with weighting factors (W-PSO-SVR),which combined support vector regression(SVR) and particle swarm optimization (PSO).The [0,1] unequal weighting factors which were achieved by the PSO search were assigned to the input variables of the model.When the unequal weighting factors were confirmed,then it established the PM2.5 predicting model.Compared with the pure SVR model and 0 or I weighting factors' SVR model,predicting results indicate that W-PSO-SVR model performs better and the predicting accuracy is higher.Besides,the W-PSO-SVR model can achieve the better effective selection of input parameters.
国家科技期刊平台
NSTL
万方数据
维普
