四川环境2025，Vol.44Issue(1)：1-8,8.DOI:10.14034/j.cnki.schj.2025.01.001
基于集成学习的北京地区空气质量站点订正模型
Correction Model for Air Quality Stations in Beijing Based on Ensemble Learning
摘要
Abstract
In order to make better use of machine learning method to reduce the prediction error of PM2.5 in Beijing.Ensemble learning strategies integrating multiple machine learning algorithms are used in the research to correct the PM2.5 forecasts of 35 stations from RMAPS-CHEM in Beijing.Taking the Huangcun Station as an example,the best correction model applicable to the southern region of Beijing is selected.The results showed that the correlation coefficient between the ensemble learning model(WE_L3)and the random forest algorithm model(RFE_L2)for 240-hour forecasting reached 0.58,an increase of 28.8％compared to 0.45 of the original model.When the period of validity is greater than 48 hours,the error of the correction model is less than that of the original model.When the period of validity is 216 hours,the correction model achieves the best performance.The Mean Absolute Error(MAE)of the ensemble learning model(WE_L3)is 14.07 μg/m3 lower than that of the original model,a decrease of 38.6％.The Root Mean Squared Error(RMSE)is 16.48 μg/m3 lower than that of the original model,a decrease of 29.6％.The MAE of the random forest algorithm model(RFG_L2)is 14.64 μ g/m3 lower than that of the original model,a decrease of 40.1％.The RMSE is 17.06 μg/m3 lower than that of the original model,a decrease of 30.7％.The spatial test showed that the corrected PM2.5 had lower deviations in stations northwest of Beijing,gradually increasing towards the southeast.The comprehensive inspection index showed that the ensemble learning correction model integrated the advantages of multiple machine learning algorithms and was the best one for Huangcun Station.关键词
PM2.5/集成学习/模式订正/睿图-化学模式/北京Key words
PM2.5/ensemble learning/correction model/RMAPS-CHEM/Beijing分类
环境科学引用本文复制引用
时跃,勾润玲,石耀辉,马一帆,王璐,骆思远,薛禄宇..基于集成学习的北京地区空气质量站点订正模型[J].四川环境,2025,44(1):1-8,8.基金项目
北京市气象局科技项目(BMBKJ202201015). （BMBKJ202201015）
