# Predictive and Prescriptive AI toward Optimizing Wildfire Suppression

arXiv: https://arxiv.org/abs/2605.04510 · HTML: https://mlsanity.com/paper/2605.04510/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 84 references that are in the map.
- First posted on arXiv 6 May 2026.

## Builds on (most cited first, 5 of 5)

- [AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data](https://mlsanity.com/paper/2507.22291/) (2025)
- [Causal Machine Learning: A Survey and Open Problems](https://mlsanity.com/paper/2206.15475/) (2022)
- [Physics-Informed Machine Learning Simulator for Wildfire Propagation](https://mlsanity.com/paper/2012.06825/) (2020)
- [Large-Scale Resilience Planning for Wildfire-Prone Electricity-System via Adaptive Robust Optimization](https://mlsanity.com/paper/2604.01232/) (2026)
- [Modeling Adversarial Wildfires for Power Grid Disruption](https://mlsanity.com/paper/2603.18473/) (2026)

## Abstract (arXiv)

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.