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A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration

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Listed:
  • Jorge Pereira

    (Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal)

  • Jérôme Mendes

    (Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal)

  • Jorge S. S. Júnior

    (Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal)

  • Carlos Viegas

    (Association for the Development of Industrial Aerodynamics, University of Coimbra, 3030-289 Coimbra, Portugal)

  • João Ruivo Paulo

    (Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Pólo II, 3030-290 Coimbra, Portugal)

Abstract

Wildfires are complex natural events that cause significant environmental and property damage, as well as human losses, every year throughout the world. In order to aid in their management and mitigate their impact, efforts have been directed towards developing decision support systems that can predict wildfire propagation. Most of the available tools for wildfire spread prediction are based on the Rothermel model that, apart from being relatively complex and computing demanding, depends on several input parameters concerning the local fuels, wind or topography, which are difficult to obtain with a minimum resolution and degree of accuracy. These factors are leading causes for the deviations between the predicted fire propagation and the real fire propagation. In this sense, this paper conducts a literature review on optimization methodologies for wildfire spread prediction based on the use of evolutionary algorithms for input parameter set calibration. In the present literature review, it was observed that the current literature on wildfire spread prediction calibration is mostly focused on methodologies based on genetic algorithms (GAs). Inline with this trend, this paper presents an application of genetic algorithms for the calibration of a set of the Rothermel model’s input parameters, namely: surface-area-to-volume ratio, fuel bed depth, fuel moisture, and midflame wind speed. The GA was validated on 37 real datasets obtained through experimental prescribed fires in controlled conditions.

Suggested Citation

  • Jorge Pereira & Jérôme Mendes & Jorge S. S. Júnior & Carlos Viegas & João Ruivo Paulo, 2022. "A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration," Mathematics, MDPI, vol. 10(3), pages 1-19, January.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:300-:d:728059
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    Citations

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    Cited by:

    1. Bárbara de Matos & Rodrigo Salles & Jérôme Mendes & Joana R. Gouveia & António J. Baptista & Pedro Moura, 2022. "A Review of Energy and Sustainability KPI-Based Monitoring and Control Methodologies on WWTPs," Mathematics, MDPI, vol. 11(1), pages 1-22, December.
    2. Esmaeil Mohammadian Bishe & Hossein Afshin & Bijan Farhanieh, 2023. "Modified Quasi-Physical Grassland Fire Spread Model: Sensitivity Analysis," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    3. Senthil Kumar Jagatheesaperumal & Khan Muhammad & Abdul Khader Jilani Saudagar & Joel J. P. C. Rodrigues, 2023. "Automated Fire Extinguishing System Using a Deep Learning Based Framework," Mathematics, MDPI, vol. 11(3), pages 1-18, January.
    4. Monica Aureliana Petcu & Liliana Ionescu-Feleaga & Bogdan-Ștefan Ionescu & Dumitru-Florin Moise, 2023. "A Decade for the Mathematics : Bibliometric Analysis of Mathematical Modeling in Economics, Ecology, and Environment," Mathematics, MDPI, vol. 11(2), pages 1-30, January.

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