Pattern recognition and modelling of virulent wildfires in Spain

María Bugallo; María Dolores Esteban; Manuel Francisco Marey-Pérez; Domingo Morales

doi:10.1071/WF23162

RESEARCH ARTICLE (Open Access)

Previous Next Contents Vol 34(2)

Pattern recognition and modelling of virulent wildfires in Spain

María Bugallo

^A ^* , María Dolores Esteban ^A , Manuel Francisco Marey-Pérez

^B and Domingo Morales ^A

+ Author Affiliations

- Author Affiliations

^A Center of Operations Research, Miguel Hernández University of Elche, Elche, Alicante, Spain.

^B Higher Polytechnic Engineering School, University of Santiago de Compostela, Santiago de Compostela, A Coruña, Spain.

^* Correspondence to: mbugallo@umh.es

International Journal of Wildland Fire 34, WF23162 https://doi.org/10.1071/WF23162

Submitted: 10 October 2023 Accepted: 28 January 2025 Published: 24 February 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Background

Driven by global warming and land use, fire dynamics are changing worldwide, increasing fire activity and its impacts on ecosystems, livelihoods and human settlements. In southern European countries, large forest fires are increasing annually and a high spatio-temporal concentration has been shown to overwhelm fire suppression systems.

Aims

Propose sound statistical criteria for grouping fire ignitions based on auxiliary information.

Methods

Our methodology combines pattern recognition and complex statistical modelling that, by capturing underlying dependencies and adapting to the nature of the data, provides a reliable statistical basis for formulating firefighting strategies.

Key results

Weekly and provincial data from 2007 to 2015 are used to illustrate the performance of the procedures.

Conclusions

Research indicates that weather conditions, the simultaneous occurrence of events and the proximity of urban areas contribute to making the Spanish landscape highly susceptible to wildfires in summer.

Implications

Risk mapping is key to fire management and efficient resource allocation.

Keywords: fire management, megafire, pattern recognition, risk map, Spanish data, statistical modelling, wildfire forecasting, zero-inflated gamma mixed model.

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