doi: 10.18178/ijml.2025.15.4.1182
A Spatiotemporal Aerosol Optical Depth Forecasting in Thailand Using Deep Learning
Chaiyo Churngam1
,
Veerasit Kaewbundit2 ,
Papis Wongchaisuwat2,*
- 1Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand
- 2Department of Industrial Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand
- Manuscript receivedOctober 30, 2024
- acceptedFebruary 15, 2025
- publishedOctober 27, 2025
Abstract
Aerosol Optical Depth (AOD) is a key parameter in atmospheric science. Accurate AOD prediction plays a crucial role for environmental management, air quality assessment, and understanding the earth climate change. It also serves as a direct indicator of particulate matter pollution, which poses significant health risks. This study addresses the need for precise AOD forecasting by implementing SwinLSTM, a multi-step spatiotemporal deep learning model. A 6-hour forecasting horizon was selected to align with practical applications, with lookback periods of 12 and 24 h. Both lookback periods yielded comparable model performance, as evidenced by the following metrics: Mean Absolute Error of 0.04, Root Mean Square Error of 0.08, Structural Similarity Index of 0.97, and Peak Signal-to-Noise Ratio ranging from 44.4 to 44.95. By enhancing our understanding of AOD and its contributing factors, we can develop more effective strategies to mitigate the negative impacts of air pollution and protect human health and the environment. Additionally, AOD forecasting can aid in understanding the impact of atmospheric particles on the Earth’s climate.
Keywords
- aerosol optical depth prediction
- air pollution
- climate change
- spatiotemporal deep learning
- SwinLSTM model
How to Cite
Chaiyo Churngam, Veerasit Kaewbundit, and Papis Wongchaisuwat, "A Spatiotemporal Aerosol Optical Depth Forecasting in Thailand Using Deep Learning," International Journal of Machine Learning, vol. 15, no. 4, pp. 78-82, 2025. https://doi.org/10.18178/ijml.2025.15.4.1182
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).