Open Access
Issue
ITM Web Conf.
Volume 68, 2024
2024 First International Conference on Artificial Intelligence: An Emerging Technology in Management (ICAETM 2024)
Article Number 01012
Number of page(s) 7
Section Engineering Technology & Management
DOI https://doi.org/10.1051/itmconf/20246801012
Published online 12 December 2024
  1. M. Castelli, F. M. Clemente, A. Popovič, S. Silva, and L. Vanneschi, “A machine learning approach to predict air quality in California,” Complexity, vol. 2020, 2020. [Google Scholar]
  2. M. Dun, Z. Xu, Y. Chen, and L. Wu, “Short-term air quality prediction based on fractional grey linear regression and support vector machine,” Mathematical problems in engineering, vol. 2020, 2020. [Google Scholar]
  3. C. Fang, H. Liu, G. Li, D. Sun, and Z. Miao, “Estimating the impact of urbanization on air quality in China using spatial regression models,” Sustainability, vol. 7, no. 11, pp. 15 570–15 592, 2015. [Google Scholar]
  4. Liang X, Li S, Zhang S, Huang H, Chen S. (2016) “PM2.5data reliability, consistency, and air quality assessment in five Chinese cities.” Journal of Geophysical Research: Atmospheres 121: 10, 220–10, 236. [Google Scholar]
  5. S. S. Ganesh, S. H. Modali, S. R. Palreddy, and P. Arulmozhivarman, “Forecasting air quality index using regression models: A case study on Delhi and Houston,” in 2017 International Conference on Trends in Electronics and Informatics (ICEI), 2017, pp. 248–254. [Google Scholar]
  6. A. Kumar and P. Goyal, “Forecasting of air quality in Delhi using principal component regression technique,” Atmospheric Pollution Research, vol. 2, no. 4, pp. 436–444, 2011. [CrossRef] [Google Scholar]
  7. C. Li, Y. Li, and Y. Bao, “Research on air quality prediction based on machine learning,” in 2021 2nd International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI), 2021, pp. 77–81. [Google Scholar]
  8. Va A, Pb G, Rab V, Soman K P. “DeepAirNet: Applying Recurrent Networks for Air Quality Prediction.” In International Conference on Computational Intelligence and Data Science (ICCIDS 2018). [Google Scholar]
  9. Prybutok V, Yi J, Mitchell D. (2000) “Comparison of neural network models with ARIMA and regression models for prediction of Houston’s daily maximum ozone concentrations.” European Journal of Operational Research 122: 31–40. [CrossRef] [Google Scholar]
  10. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M. (2016) “TensorFlow: A System for Large-Scale Machine Learning.” In OSDI 16: 265–283. [Google Scholar]
  11. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436–444. [CrossRef] [PubMed] [Google Scholar]
  12. Werbos PJ. (1990) “Backpropagation through time: what it does and how to do it.” Proceedings of the IEEE 78: 1550–60. [CrossRef] [Google Scholar]
  13. V.D. Le, T.C. Bui, S.K. Cha, Spatiotemporal deep learning model for citywide air pollution interpolation and prediction, Proc. 2020 IEEE Int. Conf. Big Data Smart [Google Scholar]
  14. A.M. Haleem, et al., Air quality assessment of some selected Hospitals within Baghdad City, Eng. Technol. J. 37 (1) (2019) 59–63. [CrossRef] [Google Scholar]
  15. Smith, John, et al. “Improving Air Quality Monitoring through IoT Sensor Networks.” IEEE Internet of Things Journal, vol. 10, no. 3, 2022, pp. 456–465. [Google Scholar]
  16. Zhang, Wei, et al. “Predicting Air Pollution Levels Using Machine Learning Techniques.” IEEE Transactions on Geoscience and Remote Sensing, vol. 20, no. 5, 2023, pp. 789–798. [Google Scholar]
  17. Chen, Li, et al. “A Comprehensive Study on Air Quality Indices and Their Applications in Urban Areas.” Environmental Science & Technology, vol. 45, no. 8, 2021, pp. 1200–1211. [Google Scholar]
  18. Kim, Min-Jae, et al. “Deep Learning-Based Air Quality Forecasting System for Smart Cities.” IEEE Access, vol. 9, 2024, pp. 23456–23468. [Google Scholar]
  19. Goyal P, Chan A, Jaiswal N. (2006) “Statistical models for the prediction of respirable suspended particulate matter in urban cities.” Atmospheric Environment 40: 2068–2077. [CrossRef] [Google Scholar]
  20. Liang X, Zou T, Guo B, Li S, Zhang H, Zhang S, Huang H, Chen S. (2015) “Assessing Beijing’s PM2.5pollution: severity, weather impact, APEC and winter heating.” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science 471: 20150257 . [CrossRef] [Google Scholar]
  21. S. Abirami, P. Chitra, Regional air quality forecasting using spatiotemporal deeplearning, J. Clean. Prod. 283 (2021), 125341. [CrossRef] [Google Scholar]
  22. D. Seng, Q. Zhang, X. Zhang, G. Chen, X. Chen, Spatiotemporal prediction of airquality based on LSTM neural network, Alex. Eng. J. 60 (2) (2021) 2021–2032. [CrossRef] [Google Scholar]
  23. I. Manisalidis, E. Stavropoulou, A. Stavropoulos, E. Bezirtzoglou, Environmental and health impacts of air pollution: a review, Front. Public Health 8 (2020). [Google Scholar]

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