EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 726 (2026) E3S Web Conf., 726 (2026) 01024 References
Open Access
Issue
E3S Web Conf.
Volume 726, 2026
The Second International Congress on Environment, Energy, and Materials for Sustainable Development Technology (IC2EM-SDT’26)
Article Number 01024
Number of page(s) 8
DOI https://doi.org/10.1051/e3sconf/202672601024
Published online 13 July 2026
  1. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012 [CrossRef] [Google Scholar]
  2. Benzekri, W., El Moussati, A., Moussaoui, O., & Berrajaa, M. (2021). Early forest fire detection and intervention system using the Internet of Things and machine learning. Symmetry, 13(3), 1–18. https://doi.org/10.3390/sym13030395 [Google Scholar]
  3. Chuvieco, E., Aguado, I., Salas, J., García, M., Yebra, M., & Oliva, P. (2020). Satellite remote sensing contributions to wildland fire science and management. Current Forestry Reports, 6(2), 81–96. https://doi.org/10.1007/s40725-020-00116-5 [Google Scholar]
  4. Cos, J., Doblas-Reyes, F., Jury, M., Marcos, R., Bretonnière, P. A., & Samsó, M. (2022). The Mediterranean climate change hotspot in the CMIP5 and CMIP6 projections. Earth System Dynamics, 13(1), 321–340. https://doi.org/10.5194/esd-13-321-2022 [Google Scholar]
  5. European Commission. (2021). Proposal for a Regulation on Artificial Intelligence (Artificial Intelligence Act). COM/2021/206 final. Brussels: European Commission. [Google Scholar]
  6. Galizia, L. F., Curt, T., Barbero, R., & Rodrigues, M. (2022). Understanding fire regimes in Europe. International Journal of Wildland Fire, 31(1), 56–66. https://doi.org/10.1071/WF20099 [Google Scholar]
  7. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. [Google Scholar]
  8. Mosavi, A., Sajedi-Hosseini, F., Choubin, B., Taromideh, F., Rahi, G., & Dineva, A. A. (2020). Susceptibility mapping of soil water erosion using machine learning models. Water, 12(7), 1995. https://doi.org/10.3390/w12071995 [Google Scholar]
  9. Pausas, J. G., Keeley, J. E., & Schwilk, D. W. (2017). Flammability as an ecological and evolutionary driver. Journal of Ecology, 105(2), 289–297. https://doi.org/10.1111/1365-2745.12691 [Google Scholar]
  10. Pinto, M. M., DaCamara, C. C., Trigo, I. F., Trigo, R. M., & Turkman, K. F. (2022). Fire danger rating over Mediterranean Europe based on fire radiative power derived from Meteosat. Natural Hazards and Earth System Sciences, 22(6), 1999–2013. https://doi.org/10.5194/nhess-22-1999-2022 [Google Scholar]
  11. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. https://doi.org/10.1145/2939672.2939778 [Google Scholar]
  12. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1, 206–215. https://doi.org/10.1038/s42256-019-0048-x [CrossRef] [Google Scholar]
  13. Shmuel, A., & Heifetz, E. (2022). Global wildfire susceptibility mapping based on machine learning models. Forests, 13(7), 1050. https://doi.org/10.3390/f13071050 [Google Scholar]
  14. Turco, M., Abatzoglou, J. T., Herrera, S., Zhukov, I., Jerez, S., & AghaKouchak, A. (2022). Anthropogenic climate change impacts exacerbate summer forest fires in California since the early 2000s. Communications Earth & Environment, 3, 1–7. https://doi.org/10.1038/s43247-022-00370-0 [Google Scholar]
  15. Van Wagner, C. E. (1987). Development and structure of the Canadian Forest Fire Weather Index System. Forestry Technical Report 35. Ottawa: Canadian Forestry Service. [Google Scholar]
  16. Vitolo, C., Di Giuseppe, F., Barnard, C., Coughlan, R., San-Miguel, J., Libertá, G., & Krzeminski, B. (2023). ERA5-based global meteorological wildfire danger. Scientific Data, 10, 1–12. https://doi.org/10.1038/s41597-023-01942-3 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.