171165 20260515163945.0 doi 10.3390/fire9040138 sideral 149252 ART-2026-149252 eng Ochoa, Clara Inferring Wildfire Ignition Causes in Spain Using Machine Learning and Explainable AI 2026 A substantial proportion of wildfires in Mediterranean regions continue to be recorded without information about the cause or source of ignition, limiting our ability to understand ignition drivers and design effective prevention strategies. In this study, we develop a spatially harmonised wildfire database for mainland Spain by integrating ignition records from the Spanish General Fire Statistics (EGIF) with fire perimeters generated from satellite images. We then apply a Random Forest classifier to infer ignition causes for events lacking cause attribution. To interpret model behaviour, we use Shapley Additive Explanation (SHAP) values at both global and local scales. Results indicate that human-caused ignitions are dominant, with intentional and negligence-related fires accounting for 52.13% of all known events, although they are associated with contrasting climatic and land-use settings. Negligence-related fires tend to occur under hot, dry and windy conditions, often in agricultural interfaces, whereas intentional fires are more frequent under cooler and wetter conditions and in areas with higher population density and land-use change. Lightning-caused fires represent a small fraction of total ignitions (3%) but exhibit a distinct climatic signature, occurring primarily in sparsely populated areas, under intermediate moisture conditions, and often leading to larger burned areas. Despite strong overall model performance (F1-score = 0.82), minority classes (e.g., lightning and fire rekindling, 0.17%) remain challenging to classify, reflecting both data imbalance and uncertainty in causal attribution. Overall, the combined use of machine learning and explainable AI provides a coherent spatial characterisation of wildfire ignition drivers across mainland Spain, highlights systematic differences among ignition causes, and identifies key limitations in existing fire cause records. This framework represents a practical step towards improving fire cause information by integrating remote sensing products with field-based fire reports, thereby supporting more targeted and evidence-based fire risk management. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/EC/H2020/101003890/EU/FIREURISK - DEVELOPING A HOLISTIC, RISK-WISE STRATEGY FOR EUROPEAN WILDFIRE MANAGEMENT/FirEUrisk This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101003890-FirEUrisk info:eu-repo/grantAgreement/ES/MICINN/JDC2022-048710-I info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Franquesa, Magí Rodrigues, Marcos Universidad de Zaragoza (orcid)0000-0002-0477-0796 Chuvieco, Emilio 3006 010 Universidad de Zaragoza Dpto. Geograf. Ordenac.Territ. Área Análisis Geográfico Regi. 9, 4 (2026), 138 [20 pp.] Fire Fire (Basel) 2571-6255 2698947 http://zaguan.unizar.es/record/171165/files/texto_completo.pdf Versión publicada 2455597 http://zaguan.unizar.es/record/171165/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:171165 articulos driver 2026-05-15-14:54:14 ARTICLE