The Development and Application of Machine Learning in Atmospheric Environment Studies

被引：18

作者：

Zheng, Lianming ^{[1
]}

Lin, Rui ^{[1
]}

Wang, Xuemei ^{[1
]}

Chen, Weihua ^{[1
]}

机构：

[1] Jinan Univ, Inst Environm & Climate Res, Guangdong Hong Kong Macau Joint Lab Collaborat In, Guangzhou 510632, Peoples R China

来源：

REMOTE SENSING | 2021年 / 13卷 / 23期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

machine learning; deep learning; atmospheric environment; nitrate wet deposition; convolutional neural network; INORGANIC NITROGEN DEPOSITION; SURFACE OZONE POLLUTION; PM2.5; CONCENTRATIONS; NEURAL-NETWORKS; HIGH-RESOLUTION; ANTHROPOGENIC EMISSIONS; PM10; PARTICULATE MATTER; YIELD PREDICTION; AIR-POLLUTION;

D O I：

10.3390/rs13234839

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Machine learning (ML) plays an important role in atmospheric environment prediction, having been widely applied in atmospheric science with significant progress in algorithms and hardware. In this paper, we present a brief overview of the development of ML models as well as their application to atmospheric environment studies. ML model performance is then compared based on the main air pollutants (i.e., PM2.5, O-3, and NO2) and model type. Moreover, we identify the key driving variables for ML models in predicting particulate matter (PM) pollutants by quantitative statistics. Additionally, a case study for wet nitrogen deposition estimation is carried out based on ML models. Finally, the prospects of ML for atmospheric prediction are discussed.

引用

页数：21

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