Modelling wildfire activity in wildland–urban interface (WUI) areas of Sardinia, Italy

Scarpa, Carla ; Elia, Mario ; D’Este, Marina ; Salis, Michele ; Rodrigues, Marcos (Universidad de Zaragoza) ; Arca, Bachisio ; Duce, Pierpaolo ; Fiori, Maria Antonella Francesca ; Pellizzaro, Grazia

Resumen: Background: Wildfire frequency, magnitude and impacts in wildland–urban interface (WUI) areas are increasing in the Mediterranean Basin.Aims: We investigated the role played by socio-economic, vegetation, climatic, and zootechnical drivers on WUI wildfire patterns (area burned and wildfire ignitions) in Sardinia, Italy. Methods: We defined WUI as the 100-m buffer area of the anthropic layers. We created a comprehensive and multi-year dataset of explanatory variables and wildfires, and then trained a set of models and evaluated their performances in predicting WUI fires. We used the best models to assess the single variable’s importance and map wildfire patterns. Key results: Random Forest and Support Vector Machine were the best performing models. In broad terms, wildfire patterns at WUI were influenced by socio-economic factors and herbaceous vegetation types. Conclusions: Machine learning models can be useful tools to predict wildfire ignitions and area burned at WUI in Mediterranean areas. Implications: Improved knowledge of the main drivers of wildfires at WUI in fire-prone Mediterranean areas can foster the development or optimisation of wildfire risk reduction and prevention strategies.

Idioma: Inglés

DOI: 10.1071/WF24109

Año: 2024

Publicado en: International Journal of Wildland Fire 33, 12 (2024), [16 pp.]
ISSN: 1049-8001

Financiación: info:eu-repo/grantAgreement/EC/H2020/101003890/EU/FIREURISK - DEVELOPING A HOLISTIC, RISK-WISE STRATEGY FOR EUROPEAN WILDFIRE MANAGEMENT/FirEUrisk
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2020-116556RA-I00

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Análisis Geográfico Regi. (Dpto. Geograf. Ordenac.Territ.)