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A Genetic Algorithm for Forest Firefighting Optimization

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Computational Science and Its Applications – ICCSA 2022 Workshops (ICCSA 2022)

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Abstract

In recent years, a large number of fires have ravaged planet Earth. A forest fire is a natural phenomenon that destroys the forest ecosystem in a given area. There are many factors that cause forest fires, for example, weather conditions, the increase of global warming and human action. Currently, there has been a growing focus on determining the ignition sources responsible for forest fires. Optimization has been widely applied in forest firefighting problems, allowing improvements in the effectiveness and speed of firefighters’ actions. The better and faster the firefighting team performs, the less damage is done. In this work, a forest firefighting resource scheduling problem is formulated in order to obtain the best ordered sequence of actions to be taken by a single firefighting resource in combating multiple ignitions. The objective is to maximize the unburned area, i.e., to minimize the burned area caused by the ignitions. A problem with 10 fire ignitions located in the district of Braga, in Portugal, was solved using a genetic algorithm. The results obtained demonstrate the usefulness and validity of this approach.

This work has been supported by FCT Fundação para a Ciência e Tecnologia within the R &D Units Project Scope UIDB/00319/2020 and PCIF/GRF/0141/2019: “O3F - An Optimization Framework to reduce Forest Fire” and the PhD grant reference UI/BD/150936/2021.

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Authors and Affiliations

  1. ALGORITMI Center, University of Minho, 4710-057, Braga, Portugal

    Marina A. Matos, Ana Maria A. C. Rocha, Lino A. Costa & Filipe Alvelos

Authors
  1. Marina A. Matos
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  2. Ana Maria A. C. Rocha
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  3. Lino A. Costa
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  4. Filipe Alvelos
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Corresponding author

Correspondence to Marina A. Matos .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Østfold University College, Halden, Norway

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  5. University of Cagliari, Cagliari, Italy

    Chiara Garau

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Matos, M.A., Rocha, A.M.A.C., Costa, L.A., Alvelos, F. (2022). A Genetic Algorithm for Forest Firefighting Optimization. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13378. Springer, Cham. https://doi.org/10.1007/978-3-031-10562-3_5

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  • DOI: https://doi.org/10.1007/978-3-031-10562-3_5

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