Development of a Hybrid Attention Transformer for Daily PM<sub>2.5</sub> Predictions in Seoul
A hybrid attention transformer (HAT) was developed for accurate daily PM<sub>2.5</sub> predictions in Seoul. The performance of the HAT was evaluated through a comparative analysis of its predictions against ground-based observations and those from a three-dimensional chemical transport...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/16/1/37 |
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| Summary: | A hybrid attention transformer (HAT) was developed for accurate daily PM<sub>2.5</sub> predictions in Seoul. The performance of the HAT was evaluated through a comparative analysis of its predictions against ground-based observations and those from a three-dimensional chemical transport model (3-D CTM). The results demonstrated that the HAT outperformed the 3-D CTM, achieving a 4.60% higher index of agreement (IOA). Additionally, the HAT exhibited 22.09% fewer errors and 82.59% lower bias compared to the 3-D CTM. Diurnal variations in PM<sub>2.5</sub> predictions from both models were also analyzed to explore the characteristics of the proposed model further. The HAT predictions closely aligned with observed PM<sub>2.5</sub> throughout the day, whereas the 3-D CTM exhibited significant diurnal variability. The importance of the input features was evaluated using the permutation method, which revealed that the previous day’s PM<sub>2.5</sub> was the most influential feature. The robustness of the HAT was further validated through a comparison with the long short-term memory (LSTM) model, which showed 18.50% lower errors and 95.91% smaller biases, even during El Niño events. These promising findings highlight the significant potential of the HAT as a cost-effective and highly accurate tool for air quality prediction. |
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| ISSN: | 2073-4433 |