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Methodology and elaboration of model for Map of Wildfire Risk: Application of Deep learning in the Regional Forest Fire Plan for the Region of Sardinia

Published: 03 April 2024 Publication History

Abstract

For civil protection purposes, risk is the probability of a calamitous event occurring that may cause harmful effects on the population, residential and productive settlements and infrastructure, within a particular area, in a given period of time. The work was carried out with the aim of being able to establish the municipal fire danger and risk index (IR), which define, respectively, the degree of danger and fire risk calculated on a regional basis and referred to the individual municipal territory, exploiting the typical functions of GIS tools and the new steps forward made by the application of artificial intelligence; however, the horizon to be reached is to be able to transform the algorithms into automated processes that can be used in platforms capable of returning outputs to end users.

References

[1]
Blasi, C., Bovio, G., Corona, P., Marchetti, M., Maturani, A., 2004. (ed.) - Fires and ecosystem complexity. From forest planning to environmental recovery. Palombi Editore, Rome. Ministero Ambiente e Tutela del Territorio and Società Botanica Italiana, Ed. Palombi & Partner, Rome.
[2]
Bertani, R., Bovio, G., Petrucci, B. 2018. Manual for the application of the A.I.B. Plan Scheme for the planning of forecasting, prevention and active fight against forest fires in national parks - (art. 8 paragraph 2 of Law no. 353 of 21 November 2000).
[3]
Regional Plan for the Prevention and Active Fight against Forest Fires (Year 2023). Calabria Region.
[4]
Brown, C.F., Brumby, S.P., Guzder-Williams, B. et al. Dynamic World, Near real-time global 10 m land use land cover mapping. Sci Data 9, 251 (2022).
[5]
National Parks Aib Plans - https://www.mase.gov.it/pagina/piani-aib-dei-parchi-nazionali
[6]
Dynamic World data - https://dynamicworld.app/ - This data is available under a Creative Commons BY-4.0 license and requires the following attribution: This dataset is produced for the Dynamic World Project by Google in partnership with National Geographic Society and the World Resources Institute.

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  1. Methodology and elaboration of model for Map of Wildfire Risk: Application of Deep learning in the Regional Forest Fire Plan for the Region of Sardinia

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    cover image ACM Conferences
    BDCAT '23: Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies
    December 2023
    187 pages
    ISBN:9798400704734
    DOI:10.1145/3632366
    Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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    Published: 03 April 2024

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    Author Tags

    1. deep learning
    2. forest
    3. wildfire
    4. civil protection
    5. hazard
    6. damage
    7. risk
    8. planning
    9. GIS

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