A Chance-Constrained Programming Model to Allocate Wildfire Initial Attack Resources for a Fire Season

Abstract

This research developed a chance-constrained two-stage stochastic programming model to support wildfire initial attack resource acquisition and location on a planning unit for a fire season. Fire growth constraints account for the interaction between fire perimeter growth and construction to prevent overestimation of resource requirements. We used this model to examine daily resource stationing budget requirements and suppression resource types and deployments within a fire planning unit. A chance constraint ensures the conditional probability of one or more fire escapes on days with ignitions below a predefined threshold. This chance-constrained approach recognizes that funding for local resources is unlikely to be sufficient for containing all fires in initial attack. For test cases, we used 1,655 fires occurring over 935 historical fire days from the Black Hills Fire Planning Unit in South Dakota. We tested our model under a variety of fire suppression assumptions to estimate appropriate daily stationing budget levels and resource allocations.