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Predicting daily firefighting personnel deployment trends in the western United States
Year: 2024
Projected increases in wildfire frequency, size, and severity may further stress already scarce firefighting resources in the western United States that are in high demand. Machine learning is a promising field with the ability to model firefighting resource usage without compromising dataset size or complexity. In this study, the Categorical Boosting (CatBoost) model was used with historical (2012-2020) wildfire data to train three models that calculate predicted daily counts of 1) total assigned personnel (total personnel), 2) assigned personnel that are at the fire (ground personnel), and…
Publication Type: Journal Article