A Bayesian-optimised network interdiction heuristic for firebreak placement under stochastic wildfire spread
Year: 2026
Type: article
Abstract: Optimal firebreak placement is a key component of preventive wildfire risk management, enabling the design of fire-resilient landscapes that mitigate expected wildfire damage. However, balancing solution quality and computational tractability at operational scales remains challenging, as the problem requires minimising expected losses over spatially explicit fire scenarios, yielding a large-scale stochastic combinatorial optimisation problem with complex spatial interactions. We formulate the firebreak placement problem as a stochastic network interdiction problem, where a decision-maker seeks to disrupt fire propagation by removing edges from a graph constructed from ensembles of simulated wildfire scenarios. We propose the Dynamic Critical Propagation Index (DCPI), a novel hybrid edge-ranking metric that integrates simulation-derived fire behaviour characteristics with dynamically updated edge betweenness centrality, capturing both empirical propagation risk and topological criticality. This metric is embedded within a beam search heuristic that efficiently explores the combinatorial solution space, while Bayesian optimisation is used to calibrate the trade-off between fire behaviour and topological information in a data-driven manner. Computational experiments on three landscape instances from Alberta, Canada (400 to 10,000 cells) show that DCPI-guided interdiction yields a lower expected burned area than two state-of-the-art methods on the instances where they could be compared, with relative improvements of 10%–12% on the 400-cell instance and 33%–39% on the 1600-cell instance. The experiments also show that the value of the fire-behaviour component and of the beam-search look-ahead depends on scale: on the 10,000-cell instance, a purely topological greedy rule performs best, indicating that at landscape scale firebreak effectiveness under random ignitions is driven mainly by compartmentalising the fuel network. Run times range from seconds to a few hours on a desktop server, which is compatible with annual or multi-year strategic planning Our results highlight the potential of combining network interdiction, simulation-based optimisation, and data-driven calibration for effective wildfire management.
Source: Ecological Informatics
Language: English
Topic: Fire effects on ecosystems
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,770
OpenAlex ID: W7223116614