Network separation modeling and quantum computing for developing wildfire fuelbreak strategy
Abstract
Fuelbreak placement is an important consideration in fire management. Historically, strategies for placing fuelbreaks have fallen on the experience of fire managers such as by following ridgelines, and recent searches for a formal placement strategy have struggled to scale to large areas. Here we present a basic strategy utilizing equal graph partitioning and quantum computing to efficiently determine placements. By posing partitioning as a quadratic constrained binary optimization problem, D-Wave's hybrid quantum optimization tool could complete the task in seconds. Results for the examined area show two alternatives to the ridgeline method in a so-called worst-case fire scenario: one with 2.9% improvement in land separation equality while clearing 76 less acres, and another with a 12.4% improvement by clearing 19 more acres. In a selected subsection, D-Wave's hybrid solver performed faster than the SCIP solver but slower than the CPLEX solver, with the prospect for increased speed-up on larger problems. These findings demonstrate the effectiveness of equal graph partitioning for fuelbreak placement and the potential of D-Wave's hybrid solvers.
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