基于XGBoost算法的WRF-Chem模式优化模拟

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Abstract The artificial intelligence algorithm XGBoost was used to build the statistical prediction algorithm model, combined with the atmospheric chemical model WRF-Chem, the simulation results of air pollutants in Beijing and the site monitoring data. The PM_2.5 and O₃ were optimized and simulated, and their characteristic contribution factors were analyzed. The results showed that the statistical forecast model of XGBoost can optimize the atmospheric pollutant concentration simulated by the atmospheric chemical model and reduce the simulation error. Moreover, the simulation concentration optimization presented the optimization characteristics of urban> suburbs> outer suburbs in Beijing area site, and the algorithm model optimized the O₃ concentration to a higher degree, and the correlation coefficient was increased by 128% after optimization. In addition, the contribution analysis of feature elements showed that CO was an important feature variable which affects the optimization of O₃ and the feature contribution scores of urban and suburban areas were as high as 1000 or more. Q2 (2m specific humidity near the ground) was an important meteorological characteristic variable that influenced the optimization of PM_2.5 and the characteristic contribution scores of urban and suburban areas were 950 and 824, respectively.

Key words： XGBoost optimization algorithm atmospheric chemistry model of WRF-Chem O₃ PM_2.5

Received: 15 April 2021

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X51

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	Articles by authors
	LI Jiang-tao
	AN Xing-qin
	LI Qing-yong
	YU Hao-min
	WANG Wei
	ZHOU Xin-yuan
	WANG Chao
	CUI Meng

Cite this article:

LI Jiang-tao,AN Xing-qin,LI Qing-yong等. Optimization of WRF-Chem model results by XGBoost algorithm[J]. CHINA ENVIRONMENTAL SCIENCECE, 2021, 41(12): 5457-5466.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2021/V41/I12/5457

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