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A simulation-optimization model for selecting the location of fuel-breaks to minimize expected losses from forest fires
- Rytwinski, Adam, Crowe, Kevin A.
- Forest ecology and management 2010 v.260 no.1 pp. 1-11
- fire break, spatial distribution, wildfires, fire spread, decision support systems, simulation models, optimization, fuels (fire ecology), operations research, stochastic processes, algorithms, calibration, boreal forests, fire behavior
- Fuel-breaks function to restrict the spread of wildfire through a forest and thereby reduce the damage to values lost when fires escape. The challenge of selecting optimal locations of fuel-breaks arises from the uncertainty over how future fires might originate and spread spatially in the future. In this research, we evaluate a new approach to addressing this uncertainty through the formulation and application of a decision support model using simulation-optimization. The problem is formulated as a combinatorial simulation-optimization problem with the objective of minimizing fire-risk subject to a limit on the total area to be used as fuel-breaks. A stochastic simulation model of fire-spread was designed to estimate the fire-risk of each candidate solution produced by a metaheuristic search algorithm. The model was tested on a forest of 220,000ha in north-western Ontario. The objective function (total fire-risk) of the solution found using the simulation-optimization model was significantly lower than the objective function of a solution found using a greedy heuristic—a spatially blind approach, by which the highest risk stands were iteratively removed until the allowable limit on fuel-breaks was reached. This significant difference indicates that the spatial relationships between selected fuel-breaks is an important factor in reducing a forest's total fire-risk. Hence, the spatial layouts of the best solutions were visually analyzed using GIS-software to illustrate patterns that result in reduced fire-risk at the landscape-scale. We conclude that this modeling approach, while computationally intensive, can be used to support improved spatial decisions on the location of fuel-breaks.