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<dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:invenio="http://invenio-software.org/elements/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><dc:identifier>doi:10.1071/WF24109</dc:identifier><dc:language>eng</dc:language><dc:creator>Scarpa, Carla</dc:creator><dc:creator>Elia, Mario</dc:creator><dc:creator>D’Este, Marina</dc:creator><dc:creator>Salis, Michele</dc:creator><dc:creator>Rodrigues, Marcos</dc:creator><dc:creator>Arca, Bachisio</dc:creator><dc:creator>Duce, Pierpaolo</dc:creator><dc:creator>Fiori, Maria Antonella Francesca</dc:creator><dc:creator>Pellizzaro, Grazia</dc:creator><dc:title>Modelling wildfire activity in wildland–urban interface (WUI) areas of Sardinia, Italy</dc:title><dc:identifier>ART-2024-141718</dc:identifier><dc:description>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.</dc:description><dc:date>2024</dc:date><dc:source>http://zaguan.unizar.es/record/148259</dc:source><dc:doi>10.1071/WF24109</dc:doi><dc:identifier>http://zaguan.unizar.es/record/148259</dc:identifier><dc:identifier>oai:zaguan.unizar.es:148259</dc:identifier><dc:relation>info:eu-repo/grantAgreement/EC/H2020/101003890/EU/FIREURISK - DEVELOPING A HOLISTIC, RISK-WISE STRATEGY FOR EUROPEAN WILDFIRE MANAGEMENT/FirEUrisk</dc:relation><dc:relation>This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101003890-FirEUrisk</dc:relation><dc:relation>info:eu-repo/grantAgreement/ES/MICINN/PID2020-116556RA-I00</dc:relation><dc:identifier.citation>International Journal of Wildland Fire 33, 12 (2024), [16 pp.]</dc:identifier.citation><dc:rights>by-nc</dc:rights><dc:rights>https://creativecommons.org/licenses/by-nc/4.0/deed.es</dc:rights><dc:rights>info:eu-repo/semantics/openAccess</dc:rights></dc:dc>

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