Air Quality Forecasting Using Big Data and Machine Learning Algorithms
Author:
Publisher
Springer Science and Business Media LLC
Subject
Atmospheric Science
Link
https://link.springer.com/content/pdf/10.1007/s13143-023-00347-z.pdf
Reference6 articles.
1. Choi, W., Ho, C.-H., Heo, J.-W., Kim, K.-Y., Kim, and S.-W., Kim. J.: Recent air quality deterioration on weekends in Seoul, South Korea: A focus on external contribution. Asia-Pac. J. Atmos. Sci. (2023). https://doi.org/10.1007/s13143-022-00287-0
2. Ejurothu, P.S.S., Mandal, S., and Thakur, M.: Forecasting PM2.5 concentration in India using a cluster based hybrid graph neural network approach. Asia-Pac J Atmos Sci (2023). https://doi.org/10.1007/s13143-022-00291-4
3. Ho, C.-H., Park, I, Kim, J., and Lee, J.-B.: PM2.5 forecast in Korea using the long short‐term memory (LSTM) model. Asia-Pac. J. Atmos. Sci. (2023). https://doi.org/10.1007/s13143-022-00293-2
4. Koo, Y.-S., Kwon, H.-Y., Bae, H., Yun, H.-Y., Choi, D.-R., Yu, S., Wang, K.-H., Koo, J.-S., Lee, Jae-Bum, Choi, M.-H., and Lee, Jeong-Beom: A development of PM2.5 forecasting system in South Korea using chemical transport modeling and machine learning. Asia-Pac. J. Atmos. Sci. (2023). https://doi.org/10.1007/s13143-023-00314-8
5. Lops, Y., Choi, Y., Mousavinezhad, S., Salman, A.K., Nelson, D.L., Singh, D.: Development of deep convolutional neural network ensemble models for 36-month ENSO forecasts. Asia-Pac. J. Atmos. Sci. (2023). https://doi.org/10.1007/s13143-023-00319-3
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