Predictive and Prescriptive AI toward Optimizing Wildfire Suppression

TL;DR

This paper presents a novel predictive and prescriptive AI framework that optimizes wildfire suppression strategies by integrating advanced optimization algorithms and machine learning to improve resource allocation and reduce wildfire impact.

Contribution

It introduces a comprehensive optimization model and a data-driven machine learning approach for wildfire suppression planning, addressing complex dynamics and resource constraints.

Findings

Algorithm scales to real-world instances

Method reduces wildfire area burned significantly

Enhances resource sharing across jurisdictions

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.