桂林理工大学学报2024，Vol.44Issue(1)：90-95,6.DOI:10.3969/j.issn.1674-9057.2024.01.012
顾及PWV的广西地区多尺度PM2.5浓度预测
Multi-scale PM2.5 concentration prediction considering PWV in Guangxi
摘要
Abstract
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.关键词
PM2.5/PWV/ARIMA/前馈神经网络Key words
PM2.5/PWV/ARIMA/feedforward neural network分类
天文与地球科学引用本文复制引用
谢劭峰,张亚博,黄良珂,魏朋志,张继洪,唐友兵..顾及PWV的广西地区多尺度PM2.5浓度预测[J].桂林理工大学学报,2024,44(1):90-95,6.基金项目
国家自然科学基金项目(41864002) （41864002）
