Modeling Individual Extended Attack Wildfire Suppression Expenditures in British Columbia

Abstract

AbstractWe developed models of suppression expenditures for individual extended attack fires in British Columbia using parametric and nonparametric machine-learning (ML) methods. Our models revealed that suppression expenditures were significantly affected by a fire’s size, proximity to the wildland–urban interface (WUI) and populated places, a weather based fire severity index, and the amount of coniferous forest cover. We also found that inflation-adjusted individual fire suppression expenditures have increased over the 1981 to 2014 study period. The ML and parametric models had similar predictive performance: the ML models had somewhat lower root mean squared errors but not on mean average errors. Better specification of fire priority as well as resource constraints might improve future model performance.