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...

Full description

Bibliographic Details
Authors: Alawode, Gbenga Lawrence, Gelabert, Pere Joan, Rodrigues, Marcos
Format: article
Status:Published version
Publication Date:2025
Country:España
Institution:Universidad de Zaragoza
Repository:Zaguán. Repositorio Digital de la Universidad de Zaragoza
OAI Identifier:oai:zaguan.unizar.es:148652
Online Access:http://zaguan.unizar.es/record/148652
Access Level:Open access
id ES_0760f903781cf1d5572ad9d2e8bc1869
oai_identifier_str oai:zaguan.unizar.es:148652
network_acronym_str ES
network_name_str España
repository_id_str
spelling A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learningAlawode, Gbenga LawrenceGelabert, Pere JoanRodrigues, MarcosWildfires 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.2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://zaguan.unizar.es/record/148652reponame:Zaguán. Repositorio Digital de la Universidad de Zaragozainstname:Universidad de ZaragozaInglésinfo:eu-repo/grantAgreement/ES/MICINN/CNS2023-144228info:eu-repo/grantAgreement/ES/MICINN/PID2020-116556RA-I00info:eu-repo/semantics/openAccessoai:zaguan.unizar.es:1486522026-05-29T13:59:51Z
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
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, Pere Joan
Rodrigues, Marcos
author Alawode, Gbenga Lawrence
author_facet Alawode, Gbenga Lawrence
Gelabert, Pere Joan
Rodrigues, Marcos
author_role author
author2 Gelabert, Pere Joan
Rodrigues, Marcos
author2_role author
author
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 http://zaguan.unizar.es/record/148652
url http://zaguan.unizar.es/record/148652
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/ES/MICINN/CNS2023-144228
info:eu-repo/grantAgreement/ES/MICINN/PID2020-116556RA-I00
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv
publisher.none.fl_str_mv
dc.source.none.fl_str_mv reponame:Zaguán. Repositorio Digital de la Universidad de Zaragoza
instname:Universidad de Zaragoza
instname_str Universidad de Zaragoza
reponame_str Zaguán. Repositorio Digital de la Universidad de Zaragoza
collection Zaguán. Repositorio Digital de la Universidad de Zaragoza
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869402979107340288
score 15,198674

Similar Items