000148652 001__ 148652
000148652 005__ 20250120165543.0
000148652 0247_ $$2doi$$a10.1080/19475705.2024.2447514
000148652 0248_ $$2sideral$$a141935
000148652 037__ $$aART-2025-141935
000148652 041__ $$aeng
000148652 100__ $$aAlawode, Gbenga Lawrence
000148652 245__ $$aA spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learning
000148652 260__ $$c2025
000148652 5060_ $$aAccess copy available to the general public$$fUnrestricted
000148652 5203_ $$aWildfires 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.
000148652 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/CNS2023-144228$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-116556RA-I00
000148652 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttp://creativecommons.org/licenses/by-nc/3.0/es/
000148652 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000148652 700__ $$aGelabert, Pere Joan
000148652 700__ $$0(orcid)0000-0002-0477-0796$$aRodrigues, Marcos$$uUniversidad de Zaragoza
000148652 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi.
000148652 773__ $$g16, 1 (2025), 2447514 [26 pp.]$$pGeomatics, natural hazards & risk$$tGeomatics, natural hazards & risk$$x1947-5705
000148652 8564_ $$s2760110$$uhttps://zaguan.unizar.es/record/148652/files/texto_completo.pdf$$yVersión publicada
000148652 8564_ $$s1107133$$uhttps://zaguan.unizar.es/record/148652/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000148652 909CO $$ooai:zaguan.unizar.es:148652$$particulos$$pdriver
000148652 951__ $$a2025-01-20-14:54:54
000148652 980__ $$aARTICLE