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Aerosol optical depth prediction based on dimension reduction methods

  • Jungkyun Lee (Department of Statistics, Chung-Ang University) ;
  • Yaeji Lim (Department of Statistics, Chung-Ang University)
  • Received : 2024.01.24
  • Accepted : 2024.02.20
  • Published : 2024.09.30

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

Acknowledgement

This research was supported by the Chung-Ang University Research Scholarship Grants in 2023.

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