计算机技术与发展2024，Vol.34Issue(1)：164-170,7.DOI:10.3969/j.issn.1673-629X.2024.01.024
基于机器学习方法的空气质量预测与影响因素识别
Air Quality Prediction and Influencing Factor Identification Based on Machine Learning Methods
摘要
Abstract
The accurate prediction of air quality index(AQI)and the identification of influencing factors are of great practical significance for air pollution prevention and control.The AQI of Beijing from the first quarter of 2014 to the second quarter of 2022 was selected as the research object to explore the influence of six major pollutants,five meteorological factors and fourteen economic variables on air quality.The DT,RF,GBDT and XGBoost models were selected to predict AQI,and the contribution of each variable to AQI was quantitatively analyzed using the stability selection method.The results show that the four model methods have excellent prediction effects,and XGBoost and RF have the best prediction effects;among the six major pollutants,PM2.5,PM10 concentration and meteorological factors,such as wind speed and pressure,have a greater influence on AQI;the influence of fourteen economic variables on AQI is quite different,among which the per capita disposable income of urban residents,tertiary industry GDP and gross industrial output value above designated size have a greater influence on AQI,while the primary industry GDP and road cargo transportation volume have a small influence.关键词
空气质量/影响因素/定量分析/机器学习/稳定性选择Key words
air quality/influencing factors/quantitative analysis/machine learning/selection of stability分类
信息技术与安全科学引用本文复制引用
李佳成,梁龙跃..基于机器学习方法的空气质量预测与影响因素识别[J].计算机技术与发展,2024,34(1):164-170,7.基金项目
国家自然科学基金项目(52000045) （52000045）
贵州省省级科技计划项目资助(黔科合基础-ZK[2022]一般076) （黔科合基础-ZK[2022]一般076）
贵州省教育厅人文社会科学研究基地项目(23RWJD030) （23RWJD030）
贵州大学经济学院创新基金资助项目(CJ2022107) （CJ2022107）
