Modelling decisions concerning the dispatch of airtankers for initial attack on forest fires in Ontario, Canada

Abstract

Airtankers are commonly used for initial attack (IA) to reduce the likelihood of wildland fires escaping containment efforts. We examined IA airtanker dispatch decisions for forest fires in Ontario, Canada, through an analysis of historical fire records from 2001 to 2019. A hurdle modelling approach that predicts the probability of airtanker(s) being dispatched and then the number of airtankers sent was used. Two different hurdle models were considered depending on the timing of the information available to the decision maker: a “fire report model” based upon the information available when a fire is first reported, and an “initial attack model” using information available at the time IA action began. Both models indicated that the most influential covariates for airtanker dispatch are the fire weather index, fuel volatility, observed fire rate of spread, fire size at IA, and cause of ignition. When evaluating the predictive ability of the models on a validation data set, the “initial attack model” performed better than the “fire report model”. Our models generally perform well when predicting none, one, or two airtankers on IA, but they generally underpredict when more than two airtankers are dispatched, which suggests risk-averse decision-making in fire management resource dispatching.