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An integrated resampling and machine learning framework for predictive analytics of large wildfires
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Orientador(es)
Resumo(s)
Accurately predicting the final burned area of wildfires at the moment of ignition is a challenging problem with important implications for disaster management and resource allocation. In this study, we apply recently developed resampling techniques, combined with machine learning algorithms, to improve predictive accuracy for rare, high-impact wildfire events. While the study focuses on wildfires, the methodology addresses a broader challenge in decision analytics: generating reliable predictions from imbalanced datasets to support informed managerial decisions. Our results demonstrate that these techniques can enhance the robustness of predictive models for extreme events, offering insights that may be relevant to other domains where accurate forecasts of rare outcomes are critical.
Descrição
Camacho, L., Catry, F. X., & Bação, F. (2026). An integrated resampling and machine learning framework for predictive analytics of large wildfires. Decision Analytics Journal, 18, Article 100674. https://doi.org/10.1016/j.dajour.2026.100674 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UID/04152/2025 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS - https://doi.org/10.54499/UID/04152/2025 (2025-01-01/2028-12-31) and UID/PRR/04152/2025 https://doi.org/10.54499/UID/PRR/04152/2025 (2025-01-01/ 2026-06-30). This work was funded by research contract CEECIND/01378/2017/CP1430/CT0005 and by FEDER funds through the Operational Competitiveness Factors Program - COMPETE and by national funds through FCT – Portuguese Foundation for Science and Technology within the scope of the project UIDB/50027/2025.
Palavras-chave
Machine learning Predictive analytics Imbalanced regression Wildfire forecasting Risk modeling Data resampling Analysis General Decision Sciences Modelling and Simulation Applied Mathematics