A comparative analysis of wildfire initial attack containment objectives and modelling strategies in Ontario, Canada

The study aims to examine the sensitivity of different time and size-based IA success definitions on model predictive accuracy and compare different modelling approaches. Using 30 years of historical fire report data from Ontario, Canada (n = 26,171), we developed logistic regression models, bagged classification trees and random forest models to predict IA success for eight different definitions. Model predictive accuracy, sensitivity and specificity were assessed on an independent validation dataset.