Generation of Optimal Fire-Line for Fighting Wildland Fires Using Genetic Algorithms

Every year all over the world, wildfires do extensive damages to the human lives, properties and natural resources. National Interagency Fire Center data provides a detailed description of the severe damages caused by the wildfires every year. Forest Fire Decision Support Systems (FFDSS) have been developed all over the world during the last thirty years with the purpose of fire detection, fire behavior prediction, and risk assessment. But optimized wildland fire containment strategies are largely lacking in these FFDSS. In this paper, decision making strategies have been formulated for wildland fire suppression so that the total burned area and hence the damage is minimized. This goal is achieved by the application of optimization tools such as the Genetic Algorithms (GA). For a given number of resources, the GA will determine their best utilization strategy so that the total area burnt is minimized. For generating optimal strategies for resource utilization, the Genetic Algorithm uses an advanced fire propagation model that predicts the propagation of wildland fires under given environmental conditions and topography. The fire-fighting strategy considered in this paper is fireline generation. Using the Genetic Algorithm, the optimal fireline is built that minimizes the area of land burned. GA also provides the proper locations of the attacking crews so that the fireline is built before the fire escapes. Using these intelligent decision making strategies, the damage caused due to a forest fire can be minimized significantly.