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Aerosol optical depth prediction based on dimension reduction methods
Communications for Statistical Applications and Methods 2024;31:521-533
Published online September 30, 2024
© 2024 Korean Statistical Society.

Jungkyun Leea, Yaeji Lim1,a

aDepartment of Statistics, Chung-Ang University, Korea
Correspondence to: 1 Department of Statistics, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea. E-mail: yaeji.lim@gmail.com
This research was supported by the Chung-Ang University Research Scholarship Grants in 2023.
Received January 24, 2024; Revised February 16, 2024; Accepted February 20, 2024.
 Abstract
As the concentration of fine dust has recently increased, numerous related studies are being conducted to address this issue. Aerosol optical depth (AOD) is a vital atmospheric parameter for measuring the optical properties of aerosols in the atmosphere, providing crucial information related to fine dust. In this paper, we apply three dimension reduction methods, nonnegative matrix factorization (NMF), empirical orthogonal functions (EOF) analysis and independent component analysis (ICA), to AOD data to analyze the patterns of fine dust in the East Asia region. Through a comparison of three dimension reduction methods, we observe that some patterns are observed in all three method, while some information are only extracted in a specific method. Additionally, we forecast AOD levels based on three methods, and compare the predictive performance of the three methodologies.
Keywords : nonnegative matrix factorization, empirical orthogonal function, independent component analysis, AOD prediction, dimension reduction