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All issues Volume 626 (2025) E3S Web Conf., 626 (2025) 01003 References
Open Access
Issue
E3S Web Conf.
Volume 626, 2025
International Conference on Energy, Infrastructure and Environmental Research (EIER 2025)
Article Number 01003
Number of page(s) 7
Section GIS and Remote Sensing in Environmental Research
DOI https://doi.org/10.1051/e3sconf/202562601003
Published online 15 April 2025
  1. C. Demir, S. Kim, Early Warnings Save Lives and Support Long-Term Sustainability, accessed: Sep. 19, 2024, https://www.undp.org/eurasia/blog/early-warnings-save-lives [Google Scholar]
  2. W.M.O. (WMO), EARLY WARNINGS FOR ALL, accessed: Sep. 19, 2024, https://library.wmo.int/idurl/4/58209 [Google Scholar]
  3. H.A.D. Nguyen, T.H. Le, M. Azzi, Q.P. Ha, Monitoring and estimation of urban emissions with low-cost sensor networks and deep learning, Ecological Informatics 82, 102750 (2024). 10.1016/j.ecoinf.2024.102750 [CrossRef] [Google Scholar]
  4. Q. Liao, M. Zhu, L. Wu, X. Pan, X. Tang, Z. Wang, Deep Learning for Air Quality Forecasts: A Review, Current Pollution Reports 6, 399 (2020). 10.1007/s40726-020-00159-z [CrossRef] [Google Scholar]
  5. G. Czibula, A. Mihai, E. Mihule¸t, NowDeepN: An Ensemble of Deep Learning Models for Weather Nowcasting Based on Radar Products’ Values Prediction, Applied Sciences 11, 125 (2021). 10.3390/app11010125 [Google Scholar]
  6. Y. Zhang, M. Long, K. Chen, L. Xing, R. Jin, M.I. Jordan, J. Wang, Skilful nowcasting of extreme precipitation with NowcastNet, Nature 619, 526 (2023). 10.1038/s41586-023-06184-4 [CrossRef] [PubMed] [Google Scholar]
  7. C. Peláez-Rodríguez, J. Pérez-Aracil, A. de LopezDiz, C. Casanova-Mateo, D. Fister, S. JiménezFernández, S. Salcedo-Sanz, Deep learning ensembles for accurate fog-related low-visibility events forecasting, Neurocomputing 549, 126435 (2023). 10.1016/j.neucom.2023.126435 [CrossRef] [Google Scholar]
  8. B. Kang, P. Zhang, Z. Gao, G. Chhipi-Shrestha, K. Hewage, R. Sadiq, Environmental assessment under uncertainty using Dempster–Shafer theory and Z-numbers, Journal of Ambient Intelligence and Humanized Computing 11, 2041 (2020). 10.1007/s12652-019-01228-y [CrossRef] [Google Scholar]
  9. F. Belmahdi, M. Lazri, F. Ouallouche, K. Labadi, R. Absi, S. Ameur, Application of Dempster-Shafer theory for optimization of precipitation classification and estimation results from remote sensing data using machine learning, Remote Sensing Applications: Society and Environment 29, 100906 (2023). 10.1016/j.rsase.2022.100906 [CrossRef] [Google Scholar]
  10. T. Gudiyangada Nachappa, S. Tavakkoli Piralilou, K. Gholamnia, O. Ghorbanzadeh, O. Rahmati, T. Blaschke, Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory, Journal of Hydrology 590, 125275 (2020). 10.1016/j.jhydrol.2020.125275 [CrossRef] [Google Scholar]
  11. J. Wang, G. Song, A Deep Spatial-Temporal Ensemble Model for Air Quality Prediction, Neurocomputing 314, 198 (2018). 10.1016/j.neucom.2018.06.049 [CrossRef] [Google Scholar]
  12. Y. Yang, H. Lv, N. Chen, A Survey on ensemble learning under the era of deep learning, Artificial Intelligence Review 56, 5545 (2023). 10.1007/s10462-022-10283-5 [CrossRef] [Google Scholar]
  13. J.F. de Oliveira, E.G. Silva, P.S. de Mattos Neto, A Hybrid System Based on Dynamic Selection for Time Series Forecasting, IEEE Transactions on Neural Networks and Learning Systems 33, 3251 (2022). 10.1109/TNNLS.2021.3051384 [CrossRef] [PubMed] [Google Scholar]
  14. M. Penza, D. Suriano, V. Pfister, M. Prato, G. Cassano, Urban Air Quality Monitoring with Networked Low-Cost Sensor-Systems, Proceedings 1, 573 (2017). 10.3390/proceedings1040573 [Google Scholar]
  15. T.H. Le, H.A.D. Nguyen, X. Barthelemy, T.T. Nguyen, Q.P. Ha, N. Jiang, H. Duc, M. Azzi, M. Riley, Visualization Platform for Multi-Scale Air Pollution Monitoring and Forecast, in 2024 IEEE/SICE International Symposium on System Integration (SII) (2024), pp. 01–06 [Google Scholar]
  16. Y. Wang, E. Coning, A. Harou, W. Jacobs, P. Joe, L. Nikitina, R. Roberts, J. Wang, J. Wilson, A. Atencia et al., Guidelines for Nowcasting Techniques (WMO, 2017), ISBN 978-92-63-11198-2 [Google Scholar]
  17. New South Wales Government, About the air quality categories, accessed: Sep. 27, 2024, https://www.environment.nsw.gov.au/topics/air/understanding-air-quality-data/air-quality-categories [Google Scholar]

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