Abstract
With the increase of global economic activities and high energy demand, many countries have raised concerns about air pollution. However, air quality prediction is a challenging issue due to the complex interaction of many factors. In this paper, we propose a multimodal approach for spatio-temporal air quality prediction. Our model learns the multimodal fusion of critical factors to predict future air quality levels. Based on the analyses of data, we also assessed the impacts of critical factors on air quality prediction. We conducted experiments on two real-world air pollution datasets. For Seoul dataset, our method achieved 11% and 8.2% improvement of the mean absolute error in long-term predictions of PM2.5 and PM10, respectively, compared to baselines. Our method also reduced the mean absolute error of PM2.5 predictions by 20% compared to the previous state-of-the-art results on China 1-year dataset.
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Seoul Autonomous Weather System.
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Acknowledgments
This work was supported by the New Industry Promotion Program (1415158216, Development of Front/Side Camera Sensor for Autonomous Vehicle) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea).
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Bui, TC. et al. (2021). STAR: Spatio-Temporal Prediction of Air Quality Using a Multimodal Approach. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1251. Springer, Cham. https://doi.org/10.1007/978-3-030-55187-2_31
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DOI: https://doi.org/10.1007/978-3-030-55187-2_31
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