Multi-scale PM2.5 concentration prediction considering PWV in Guangxi
The existing smog-haze forecast methods less consider about the influence of precipitable water va-por,and most of the prediction methods do not effectively handle the model regression residuals,so the predic-tion accuracy is not very high.For these issues,the PM2.5 daily mean value data of four cities in Guangxi(Nan-ning,Guilin,Wuzhou and Baise)in 2017 combining with the factors of air pollutants,meteorological factors and precipitable water vapor(PWV),ARIMA models of the whole year and each quarter are respectively estab-lished to make short-term prediction of daily average PM2.5 concentration in this region.The forecast residuals of ARIMA model are respectively fitted with the feedforward neural network radical basis function(RBF)and multi-layer perceptron(MLP)in order to optimize ARIMA model.The results show,except Guilin,the predic-tion effect of quarterly ARIMA model is better than the annual ARIMA model,and the quarterly ARIMA-MLP neural network prediction accuracy is better than the quarterly ARIMA model,indicating that this kind of model can be used for regional PM2.5 concentration prediction.
国家科技期刊平台
NSTL
万方数据
