Development of a Deep Learning-based Midterm PM2.5 Prediction Model Adapting to Trend Changes 


Vol. 13,  No. 6, pp. 251-259, Jun.  2024
https://doi.org/10.3745/TKIPS.2024.13.6.251


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  Abstract

Fine particulate matter, especially PM2.5 with a diameter of less than 2.5 micrometers, poses significant health and economic risks. This study focuses on the Seoul region of South Korea, aiming to analyze PM2.5 data and trends from 2017 to 2022 and develop a mid-term prediction model for PM2.5 concentrations. Utilizing collected and produced air quality and weather data, reanalysis data, and numerical model prediction data, this research proposes an ensemble evaluation method capable of adapting to trend changes. The ensemble method proposed in this study demonstrated superior performance in predicting PM2.5 concentrations, outperforming existing models by an average F1 Score of approximately 42.16% in 2019, 58.92% in 2021, and 34.79% in 2022 for future 3 to 6-day predictions. The model maintains performance under changing environmental conditions, offering stable predictions and presenting a mid-term prediction model that extends beyond the capabilities of existing deep learning-based short-term PM2.5 forecasts.

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[IEEE Style]

D. J. Min, H. Kim, S. Lee, "Development of a Deep Learning-based Midterm PM2.5 Prediction Model Adapting to Trend Changes," The Transactions of the Korea Information Processing Society, vol. 13, no. 6, pp. 251-259, 2024. DOI: https://doi.org/10.3745/TKIPS.2024.13.6.251.

[ACM Style]

Dong Jun Min, Hyerim Kim, and Sangkyun Lee. 2024. Development of a Deep Learning-based Midterm PM2.5 Prediction Model Adapting to Trend Changes. The Transactions of the Korea Information Processing Society, 13, 6, (2024), 251-259. DOI: https://doi.org/10.3745/TKIPS.2024.13.6.251.

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