Machine learning approach for the ground level aerosol concentration analysis

Ekaterina Nagovitsyna; Anna Luzhetskaya; Vassily Poddubny; Aleksey Shchelkanov; Vadim Gadelshin

doi:10.1117/12.2603435

15 December 2021 Machine learning approach for the ground level aerosol concentration analysis

Ekaterina Nagovitsyna, Anna Luzhetskaya, Vassily Poddubny, Aleksey Shchelkanov, Vadim Gadelshin

Author Affiliations +

Proceedings Volume 11916, 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics; 119163X (2021) https://doi.org/10.1117/12.2603435
Event: 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics, 2021, Moscow, Russian Federation

Abstract

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 ERA5 reanalysis database were chosen as predictors. The problem was solved using an ensemble machine learning algorithm - a random forest.

Citation Download Citation

Ekaterina Nagovitsyna, Anna Luzhetskaya, Vassily Poddubny, Aleksey Shchelkanov, and Vadim Gadelshin "Machine learning approach for the ground level aerosol concentration analysis", Proc. SPIE 11916, 27th International Symposium on Atmospheric and Ocean Optics, Atmospheric Physics, 119163X (15 December 2021); https://doi.org/10.1117/12.2603435

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