A Spatial Optimization Approach to Finding Locations for Wildfire Fuel Treatments




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Wildfires are a major concern in many parts of the world. In order to minimize the threat posed by wildfires, prior to the ignition of a wildfire forest managers use fuel management activities to modify the volume and structure of fuel in the landscape with the goal of lessening the intensity and rate of spread of any wildfires that do ignite. This implies that one of the tasks of forest managers is to identify the spatial locations where forest fuels will be treated. Quantitative spatial optimization techniques have been proposed as a means of finding optimal or near-optimal locations for forest fuel management activities. The aim of this research is to develop a new heuristic algorithm that builds upon the earlier work of Valdez-Lazalde (2001) in this area. This earlier study proposed a method of quantifying the benefit obtained from a proposed fuel treatment activity, and used that metric in a steepest-decent type heuristic approach to develop proposed fuel treatment plans. This study replaces the steepest decent approach with a genetic algorithm. It is found that the genetic approach consistently produces better-performing fuel treatment plans than does either the steepest decent model or a series of fire management experts who were asked to develop fuel management plans for the same hypothetical scenarios as were presented to the genetic and steepest decent models.



Wildfires—Prevention and control, Fuelwood, Spatial analysis (Statistics), Genetic algorithms, Heuristic algorithms



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