Open Access
| Issue |
ITM Web Conf.
Volume 79, 2025
International Conference on Knowledge Engineering and Information Systems (KEIS-2025)
|
|
|---|---|---|
| Article Number | 01048 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/itmconf/20257901048 | |
| Published online | 08 October 2025 | |
- L. Chen, J. Wang, H. Wang, T. Jin, Urban air quality assessment by fusing spatial and temporal data from multiple study sources using refined estimation methods. ISPRS Int. J. Geo-Inf. 11, 330 (2022). https://doi.org/10.3390/ijgi11060330 [Google Scholar]
- B. Qi, Y. Jiang, H. Wang, J. Jin, Multi-source PM2.5 prediction model based on fusion of graph attention networks and multiple time periods. IEEE Access 12, 57603–57612 (2024). https://doi.org/10.1109/ACCESS.2024.3435678 [Google Scholar]
- X. Chen, Y. Hu, F. Dong, K. Chen, H. Xia, A multigraph spatial-temporal attention network for airquality prediction. Process Saf. Environ. Prot. 181, 442–451 (2024). https://doi.org/10.1016/j.psep.2023.11.040 [Google Scholar]
- N.H. Van, P. Van Thanh, D.N. Tran, D.T. Tran, A new model of air quality prediction using lightweight machine learning. Int. J. Environ. Sci. Technol. 20, 2983–2994 (2023). https://doi.org/10.1007/s13762-022-04185-w [Google Scholar]
- H. Wang, L. Zhang, R. Wu, Y. Cen, Spatiotemporal fusion of meteorological factors for multisite PM2.5 predictions: A deep learning and timevariant graph approach. Environ. Res. 239, 117286 (2023). https://doi.org/10.1016/j.envres.2023.117286 [Google Scholar]
- M.A. Bhatti, Z. Song, U.A. Bhatti, M.S.M, AIoT- driven multi-source sensor emission monitoring and forecasting using multi-source sensor integration with reduced noise series decomposition. J. Cloud Comput. 13, 65 (2024). https://doi.org/10.1186/s13677-024-00598-9 [Google Scholar]
- H. Xia, X. Chen, Z. Wang, X. Chen, F. Dong, A multi-modal deep-learning air quality prediction method based on multi-station time-series data and remote-sensing images: Case study of Beijing and Tianjin. Entropy 26, 91 (2024). https://doi.org/10.3390/e26010091 [Google Scholar]
- S. Raj, J. Smith, E. Hayes, Hybrid graph convolutional LSTM model for spatio-temporal air quality transfer learning. Air Qual. Atmos. Health, 1–21 (2025). https://doi.org/10.1007/s11869-025-01713-8 [Google Scholar]
- Y. Huang, X. Zhu, R. Wang, Y. Xie, S. Fong, A dynamic global–local spatiotemporal graph framework for multi-city PM2.5 long-term forecasting. Remote Sens. 17, 2750 (2025). https://doi.org/10.3390/rs17162750 [Google Scholar]
- N.N. Maltare, S. Vahora, Air quality index prediction using machine learning for Ahmedabad city. Digit. Chem. Eng. 7, 100093 (2023). https://doi.org/10.1016/j.dche.2023.100093 [Google Scholar]
- S. Dalal, U.K. Lilhore, N. Faujdar, S. Samiya, V. Jaglan, R. Alroobaea, M. Shaheen, F. Ahmad, Optimising air quality prediction in smart cities with hybrid particle swarm optimization-long-short term memory-recurrent neural network model. IET Smart Cities 6, 156–179 (2024). https://doi.org/10.1049/smc2.12080 [Google Scholar]
- J. Han, H. Liu, H. Xiong, J. Yang, Semi-supervised air quality forecasting via self-supervised hierarchical graph neural network. IEEE Trans. Knowl. Data Eng. 35, 5230–5243 (2022). https://doi.org/10.1109/TKDE.2022.3149815 [Google Scholar]
- F. Jiang, J. Ma, Environmental justice in the 15- minute city: Assessing air pollution exposure inequalities through machine learning and spatial network analysis. Smart Cities 8, 53 (2025). https://doi.org/10.3390/smartcities8020053 [Google Scholar]
- D. Martínez, L. Po, R. Trillo-Lado, J.R. Viqueira, TAQE: A data modeling framework for traffic and air quality applications in smart cities, In International Conference on Conceptual Structures, Springer International Publishing, Cham, September 11 (2022), 25–40 [Google Scholar]
- B. Liu, Z. Qi, L. Gao, Enhanced air quality prediction through spatio-temporal feature sxtraction and fusion: a self-tuning hybrid approach with GCN and GRU. Water Air Soil Pollut. 23, 532 (2024). https://doi.org/10.1007/s11270-024-07346-4 [Google Scholar]
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