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

共 50 条

[21] A data-driven approach to forecasting ground-level ozone concentration
Marvin, Dario
Nespoli, Lorenzo
Strepparava, Davide
Medici, Vasco
INTERNATIONAL JOURNAL OF FORECASTING, 2022, 38 (03) : 970 - 987
[22] Quality monitoring in vibro ground improvement – A hybrid machine learning approach
Terbuch A.
Zöhrer A.
Winter V.
O‘Leary P.
Khalili-Motlagh-Kasmaei N.
Steiner G.
Geomechanik und Tunnelbau, 2022, 15 (05): : 658 - 664
[23] Evaluation of different machine learning approaches for predicting high concentration episodes of ground-level ozone: A case study in Catalonia, Spain
Vicente, D. J.
Salazar, F.
Lopez-Chacon, S. R.
Soriano, C.
Martin-Vide, J.
ATMOSPHERIC POLLUTION RESEARCH, 2024, 15 (03)
[24] Estimating ground-level high-resolution ozone concentration across China using a stacked machine-learning method
Li, Zizheng
Wang, Weihang
He, Qingqing
Chen, Xiuzhen
Huang, Jiejun
Zhang, Ming
ATMOSPHERIC POLLUTION RESEARCH, 2024, 15 (06)
[25] Machine learning for improved data analysis of biological aerosol using the WIBS
Ruske, Simon
Topping, David O.
Foot, Virginia E.
Morse, Andrew P.
Gallagher, Martin W.
ATMOSPHERIC MEASUREMENT TECHNIQUES, 2018, 11 (11) : 6203 - 6230
[26] Predicting open education competency level: A machine learning approach
Ibarra-Vazquez, Gerardo
Ramirez-Montoya, Maria Soledad
Buenestado-Fernandez, Mariana
Olague, Gustavo
HELIYON, 2023, 9 (11)
[27] MLEE: Method Level Energy Estimation - A machine learning approach
Alvi, Hamza Mustafa
Majeed, Hammad
Mujtaba, Hasan
Beg, Mirza Omer
SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS, 2021, 32
[28] Machine learning approach to gate-level evolvable hardware
Iba, H
Iwata, M
Higuchi, T
EVOLVABLE SYSTEMS: FROM BIOLOGY TO HARDWARE, 1997, 1259 : 327 - 343
[29] A Machine Learning Approach for Classifying the Default Bug Severity Level
Aburakhia, Abdalrahman
Alshayeb, Mohammad
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2024, 49 (09) : 13131 - 13148
[30] Aerosol Characterization Using Machine Learning
Natraj, Vijay
Chen, Sihe
Zeng, Zhao-Cheng
Yung, Yuk L.
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 6638 - 6641

← 1 2 3 4 5 →