基于MODIS数据与多机器学习法的日PM<sub>2.5</sub>模拟研究

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Abstract In order to further understand the spatial distribution of PM_2.5 on the ground_, based on the PM_2.5 measured data in 2019, MCD19A2 aerosol optical depth product of the Moderate Resolution Imaging Spectroradiometer (MODIS) at the L3 level, taking Shandong Province as the study area, and fully considering the factors including population, terrain, and weather. The daily PM_2.5 in 2019 was simulated by using the four machine learning algorithms of Random Forest (RF), Support Vector Regression (SVR), Back Propagation Neural Network (BPNN), and Deep Neural Networks (DNN). The result shows the RMSE and MAE values of the RF are 12.67 and 6.62, respectively, which are better than BPNN, SVR and DNN models. RF is most suitable for the daily PM_2.5 simulation in Shandong Province.

Key words： remote sensing PM_2.5 AOD machine learning

Received: 24 November 2021

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X513

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	XU Fa-zhao
	LI Jing
	CHU Xin-de
	MAN Yuan-wei

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XU Fa-zhao,LI Jing,CHU Xin-de等. Simulation of daily PM_2.5 based on MODIS data and multi-machine learning method[J]. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(6): 2523-2529.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2022/V42/I6/2523

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