摘要
在我国当前大气重污染的环境下,PM2.5浓度的预警预报工作显得尤为重要.由于PM2.5浓度时间序列具有高度复杂性与随机性等特点,且传统的PM2.5浓度分解集成预测方法没有考虑空气质量因素与气象因素的信息,仅靠PM2.5浓度的历史值难以准确对其精准预测.本文在对历史数据的分解下,对高频数据引入TPE-XGBOOST模型,对低频数据引入LassoLars模型,结合空气质量因素与气象因素反映分解特征的变化趋势,对PM2.5浓度时间序列展开预测研究.通过实验,该组合模型显示出了良好的预测效果,且相对于单一分解集成预测模型有较大的预测精度提升.
Because of serious atmospheric pollution,the early warning and forecasting of PM2.5 concentration is particularly important.Due to the high complexity and randomness of the time series of PM2.5concentration,the traditional integrated PM2.5 concentration decomposition prediction method does not take the air quality and meteorological factors into account.Thus,it is difficult to predict the PM2.5concentration accurately only by the historical value.By decomposing the historical data,this paper introduced the TPE-XGBOOST model for high-frequency data and LassoLars model for low-frequency data,combined air quality and meteorological factors to reflect the variation trend of decomposition characteristics,and made prediction for the time series of PM2.5 concentration.Through the experiment,the model shows good prediction effect,and has higher prediction accuracy compared with the single decomposition integrated prediction model.
作者
翁克瑞
刘淼
刘钱
WENG Kerui;LIU Miao;LIU Qian(School of Economics and Management,China University of Geosciences,Wuhan 430074,China)
出处
《系统工程理论与实践》
EI
CSSCI
CSCD
北大核心
2020年第3期748-760,共13页
Systems Engineering-Theory & Practice
基金
国家自然科学基金(71874163).
