Predictive and Prescriptive AI toward Optimizing Wildfire Suppression

• URL: http://arxiv.org/abs/2605.04510v2
• Date: Thu, 07 May 2026 17:49:30 GMT
• Title: Predictive and Prescriptive AI toward Optimizing Wildfire Suppression
• Abstract summary: This paper develops a predictive and prescriptive approach to jointly optimize crew assignments and wildfire suppression.<n>The problem features a discrete resource-allocation structure with endogenous wildfire demand and non-linear wildfire dynamics.<n>We develop a two-sided branch-and-price-and-cut algorithm based on: (i) a two-sided column generation scheme that generates fire suppression plans and crew routes iteratively; (ii) a new family of cuts exploiting the knapsack structure of the linking constraints; and (iii) novel branching rules to accommodate non-linear wildfire dynamics.
• Score: 39.19025792909854
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intense wildfire seasons require critical prioritization decisions to allocate scarce suppression resources over a dispersed geographical area. This paper develops a predictive and prescriptive approach to jointly optimize crew assignments and wildfire suppression. The problem features a discrete resource-allocation structure with endogenous wildfire demand and non-linear wildfire dynamics. We formulate an integer optimization model with crew assignments on a time-space-rest network, wildfire dynamics on a time-state network, and linking constraints between them. We develop a two-sided branch-and-price-and-cut algorithm based on: (i) a two-sided column generation scheme that generates fire suppression plans and crew routes iteratively; (ii) a new family of cuts exploiting the knapsack structure of the linking constraints; and (iii) novel branching rules to accommodate non-linear wildfire dynamics. We also propose a data-driven double machine learning approach to estimate wildfire spread as a function of covariate information and suppression efforts, mitigating observed confounding between historical crew assignments and wildfire growth. Extensive computational experiments show that the optimization algorithm scales to otherwise intractable real-world instances; and that the methodology can enhance suppression effectiveness in practice, resulting in significant reductions in area burned over a wildfire season and guiding resource sharing across wildfire jurisdictions.

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