Machine learning approach for the ground level aerosol concentration analysis

被引：0

作者：

Nagovitsyna, Ekaterina ^{[1
,2
]}

Luzhetskaya, Anna ^{[1
]}

Poddubny, Vassily ^{[1
]}

Shchelkanov, Aleksey ^{[1
]}

Gadelshin, Vadim ^{[2
,3
]}

机构：

[1] Russian Acad Sci, Inst Ind Ecol, Ural Branch, Ekaterinburg, Russia

[2] Ural Fed Univ, Ekaterinburg, Russia

[3] Johannes Gutenberg Univ Mainz, Mainz, Germany

来源：

27TH INTERNATIONAL SYMPOSIUM ON ATMOSPHERIC AND OCEAN OPTICS, ATMOSPHERIC PHYSICS | 2021年 / 11916卷

关键词：

atmospheric aerosol; particulate matter; random forest algorithm; PM2.5;

D O I：

10.1117/12.2603435

中图分类号：

P4 [大气科学（气象学）];

学科分类号：

0706 ; 070601 ;

摘要：

A machine learning approach to solve a multiple regression problem is considered. Mass concentration of aerosol particles in the surface layer of the atmosphere was used as a dependent variable. The aerosol optical depth of the atmosphere and a number of meteorological parameters from the ECMWF ERAS reanalysis database were chosen as predictors. The problem was solved using an ensemble machine learning algorithm - a random forest.

引用

页数：5

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