A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learning

Wildfires are particularly prevalent in the Mediterranean, being expected to increase in frequency due to the expected increase in regional temperatures and decrease in precipitation. Effectively suppressing large wildfires requires a thorough understanding of containment opportunities across landsc...

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Detalhes bibliográficos
Autores: Alawode, Gbenga Lawrence, Gelabert Vadillo, Pere Joan, Rodrigues, Marcos
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10459.1/467569
Acesso em linha:https://doi.org/10.1080/19475705.2024.2447514
https://hdl.handle.net/10459.1/467569
Access Level:acceso abierto
Palavra-chave:Megafires
Random forest
Geospatial
Fire suppression
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repository_id_str
spelling A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learningAlawode, Gbenga LawrenceGelabert Vadillo, Pere JoanRodrigues, MarcosMegafiresRandom forestGeospatialFire suppressionWildfires are particularly prevalent in the Mediterranean, being expected to increase in frequency due to the expected increase in regional temperatures and decrease in precipitation. Effectively suppressing large wildfires requires a thorough understanding of containment opportunities across landscapes, to which empirical spatial modelling can contribute largely. The previous containment model in Catalonia failed to account for the crucial roles of weather conditions, lacked temporal prediction and could not forecast windows for containment opportunities, prompting this research. We employed a detailed geospatial approach to assess the spatial-temporal variations in containment probability for escaped wildfires in Catalonia. Using machine learning algorithms, geospatial data, and 124 historical wildfire perimeters from 2000 to 2015, we developed a predictive model with high accuracy (Area Under the Receiver Operating Characteristics Curve = 0.81 +/- 0.03) over 32,108 km2 at a 30-meter resolution. Our analysis identified agricultural plains near non-burnable barriers, such as major road corridors, as having the highest containment probability. Conversely, steep mountainous regions with limited accessibility exhibited lower containment success rates. We also found temperature and windspeed to be critical factors influencing containment success. These findings inform optimal firefighting resource allocation and contribute to strategic fuel management initiatives to enhance firefighting operations.The funding for the research was provided by the European Union’s scholarship support through the Erasmus Mundus Joint Master’s Degree program.Taylor & Francis2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1080/19475705.2024.2447514https://hdl.handle.net/10459.1/467569reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a https://doi.org/10.1080/19475705.2024.2447514Geomatics Natural Hazards & Risk, 2025, vol. 16, núm. 1, p. 1-25cc-by-nc (c) Alawode, 2025Attribution-NonCommercial 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/4.0/oai:recercat.cat:10459.1/4675692026-05-29T05:05:01Z
dc.title.none.fl_str_mv A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learning
title A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learning
spellingShingle A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learning
Alawode, Gbenga Lawrence
Megafires
Random forest
Geospatial
Fire suppression
title_short A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learning
title_full A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learning
title_fullStr A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learning
title_full_unstemmed A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learning
title_sort A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learning
dc.creator.none.fl_str_mv Alawode, Gbenga Lawrence
Gelabert Vadillo, Pere Joan
Rodrigues, Marcos
author Alawode, Gbenga Lawrence
author_facet Alawode, Gbenga Lawrence
Gelabert Vadillo, Pere Joan
Rodrigues, Marcos
author_role author
author2 Gelabert Vadillo, Pere Joan
Rodrigues, Marcos
author2_role author
author
dc.subject.none.fl_str_mv Megafires
Random forest
Geospatial
Fire suppression
topic Megafires
Random forest
Geospatial
Fire suppression
description Wildfires are particularly prevalent in the Mediterranean, being expected to increase in frequency due to the expected increase in regional temperatures and decrease in precipitation. Effectively suppressing large wildfires requires a thorough understanding of containment opportunities across landscapes, to which empirical spatial modelling can contribute largely. The previous containment model in Catalonia failed to account for the crucial roles of weather conditions, lacked temporal prediction and could not forecast windows for containment opportunities, prompting this research. We employed a detailed geospatial approach to assess the spatial-temporal variations in containment probability for escaped wildfires in Catalonia. Using machine learning algorithms, geospatial data, and 124 historical wildfire perimeters from 2000 to 2015, we developed a predictive model with high accuracy (Area Under the Receiver Operating Characteristics Curve = 0.81 +/- 0.03) over 32,108 km2 at a 30-meter resolution. Our analysis identified agricultural plains near non-burnable barriers, such as major road corridors, as having the highest containment probability. Conversely, steep mountainous regions with limited accessibility exhibited lower containment success rates. We also found temperature and windspeed to be critical factors influencing containment success. These findings inform optimal firefighting resource allocation and contribute to strategic fuel management initiatives to enhance firefighting operations.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1080/19475705.2024.2447514
https://hdl.handle.net/10459.1/467569
url https://doi.org/10.1080/19475705.2024.2447514
https://hdl.handle.net/10459.1/467569
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a https://doi.org/10.1080/19475705.2024.2447514
Geomatics Natural Hazards & Risk, 2025, vol. 16, núm. 1, p. 1-25
dc.rights.none.fl_str_mv cc-by-nc (c) Alawode, 2025
Attribution-NonCommercial 4.0 International
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/4.0/
rights_invalid_str_mv cc-by-nc (c) Alawode, 2025
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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