Predicting Fire Brigades Operational Breakdowns: A Real Case Study

Selene Cerna, Christophe Guyeux, Guillaume Royer, Céline Chevallier and Guillaume Plumerel
Additional contact information
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

Mathematics, 2020, vol. 8, issue 8, 1-19

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.

Keywords: operational breakdowns; forecasting disruptions; firemen; breakage calculation; XGBoost (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/8/8/1383/pdf (application/pdf)
https://www.mdpi.com/2227-7390/8/8/1383/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:8:y:2020:i:8:p:1383-:d:400426

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().