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Predicting Fire Brigades Operational Breakdowns: A Real Case Study

Author

Listed:
  • Selene Cerna

    (Femto-ST Institute, University of Bourgogne Franche-Comté, UBFC, CNRS, 90000 Belfort, France)

  • Christophe Guyeux

    (Femto-ST Institute, University of Bourgogne Franche-Comté, UBFC, CNRS, 90000 Belfort, France)

  • Guillaume Royer

    (SDIS25—Service Départemental d’Incendie et de Secours du Doubs, 25000 Besançon, France)

  • Céline Chevallier

    (SDIS25—Service Départemental d’Incendie et de Secours du Doubs, 25000 Besançon, France)

  • Guillaume Plumerel

    (SDIS25—Service Départemental d’Incendie et de Secours du Doubs, 25000 Besançon, France)

Abstract

Over the years, fire departments have been searching for methods to identify their operational disruptions and establish strategies that allow them to efficiently organize their resources. The present work develops a methodology for breakage calculation and another for predicting disruptions based on machine learning techniques. The main objective is to establish indicators to identify the failures due to the temporal state of the organization in the human and vehicular material. Likewise, by forecasting disruptions, to determine strategies for the deployment or acquisition of the necessary armament. This would allow improving operational resilience and increasing the efficiency of the firemen over time. The methodology was applied to the Departmental Fire and Rescue Doubs (SDIS25) in France. However, it is generic enough to be extended and adapted to other fire departments. Considering a historic of breakdowns of 2017 and 2018, the best predictions of public service breakdowns for the year 2019, presented a root mean squared error of 2.5602 and a mean absolute error of 2.0240 on average with the XGBoost technique.

Suggested Citation

  • Selene Cerna & Christophe Guyeux & Guillaume Royer & Céline Chevallier & Guillaume Plumerel, 2020. "Predicting Fire Brigades Operational Breakdowns: A Real Case Study," Mathematics, MDPI, vol. 8(8), pages 1-19, August.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1383-:d:400426
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    References listed on IDEAS

    as
    1. Carvalho, A.S. & Captivo, M.E. & Marques, I., 2020. "Integrating the ambulance dispatching and relocation problems to maximize system’s preparedness," European Journal of Operational Research, Elsevier, vol. 283(3), pages 1064-1080.
    2. Fonseca Morello, Thiago & Marchetti Ramos, Rossano & O. Anderson, Liana & Owen, Nathan & Rosan, Thais Michele & Steil, Lara, 2020. "Predicting fires for policy making: Improving accuracy of fire brigade allocation in the Brazilian Amazon," Ecological Economics, Elsevier, vol. 169(C).
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