| 应用机器学习算法的成都市冬季空气污染预报研究 |
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| 引用本文： | 孙苏琪,王式功,罗彬,杜云松,张巍.应用机器学习算法的成都市冬季空气污染预报研究[J].气象与环境学报,2020,36(2):98-104. |
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| 作者姓名： | 孙苏琪 王式功 罗彬 杜云松 张巍 |
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| 作者单位： | 1. 成都信息工程大学大气科学学院/高原大气与环境四川省重点实验室, 四川 成都 6102252. 贵州省遵义院士工作 中心 气候环境与医疗康养重点实验室, 贵州 遵义 5630003. 四川省环境政策研究与规划院, 四川 成都 6100414. 四川省生态环境监测总站, 四川 成都 610091 |
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| 基金项目： | 国家重点研发计划;应对重点专项;国家自然科学基金;国家科技重大专项 |
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| 摘 要： | 利用2014年3月至2017年2月成都市8个环境监测站的PM 2.5、PM 10、SO 2、NO 2、CO、O 3共6种污染物质量浓度资料以及T639全球中期数值预报模式产品,采用两种机器学习算法—递归特征消除法(Recursive feature elimination,RFE)和随机森林方法,构建了成都市冬季5种(O 3除外,因为其冬季污染较轻)污染物浓度的预报模型,并对模型的预报效果进行了评价。结果表明:基于RFE模型的5种污染物预报值与实测值的均方根误差值分别为47.58μg·m^-3、72.10μg·m^-3、8.87μ·m-3、0.59 mg·m^-3、19.84μg·m^-3;基于随机森林模型的5种污染物预报值与实测值均方根误差值分别为23.94μg·m^-3、20.98μg·m^-3、2.40μg·m^-3、0.16 mg·m^-3、8.09μg·m^-3,随机森林模型对各污染物浓度的预报效果均优于RFE模型,说明该预报方法性能良好,可为成都市冬季空气质量业务化预报提供技术支持和防控依据。
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| 关 键 词： | 空气污染预报 递归特征消除法 随机森林方法 |
| 收稿时间： | 2019-03-25 |
Air pollution forecast in winter based on machine learning method in Chengdu |
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| Su-qi SUN,Shi-gong WANG,Bin LUO,Yun-song DU,Wei ZHANG.Air pollution forecast in winter based on machine learning method in Chengdu[J].Journal of Meteorology and Environment,2020,36(2):98-104. |
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| Authors: | Su-qi SUN Shi-gong WANG Bin LUO Yun-song DU Wei ZHANG |
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| Institution: | 1. Plateau Atmosphere and Environment Key Laboratory of Sichuan Province/College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China2. Zunyi Academician Center, Chinese Academy of Sciences & Chinese Academy of Engineering, Zunyi 563000, China3. Sichuan Province Environmental Policy Research and Planning Institute, Chengdu 610041, China4. Sichuan Environmental Monitoring Center, Chengdu 610091, China |
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| Abstract: | In this paper, based on the PM2.5, PM10, SO2, NO2, CO, O3 pollutant concentration data from 8 environmental monitoring stations of Chengdu and the T639 global medium-term numerical forecast model products from March of 2014 to February of 2017, the forecast model for the five of the above-mentioned six pollutant except O3 in winter in Chengdu was built by using the recursive feature elimination (RFE) and the random forest method which are superior to the traditional statistical method, and its forecasting performance was assessed.The results show that the mean squared error (MSE) of the values of five pollutants forecasted by the RFE model are 47.58 μg·m-3, 72.10 μg·m-3, 8.87 μ·m-3, 0.59 mg·m-3, 19.84 μg·m-3, and those by the random forest model are 23.94 μg·m-3, 20.98 μg·m-3, 2.40 μg·m-3, 0.16 mg·m-3, 8.09 μg·m-3, which proves that the performance of the random forest model is better than that of the RFE model in the pollutant concentration forecast, indicating that the random forest method has a good performance and can provide the technical support for the air quality forecast business and the basis for the air pollution prevention and control in winter in Chengdu. |
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| Keywords: | Air pollution forecast Recursive feature elimination Random forecast method |
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