A hybrid ant colony based system for assist the prevention and mitigation of wildfires in forests

The control and planning of wildfires is a challenging research task. The deep impact caused by the wildfire in natural ecosystems, such as woodlands and forests, makes essential the development of new methodologies and techniques to mitigate the wildfire expansion. In this paper we propose a framework for aiding to determine the best plan to attack dynamic wildfires composed of several seats of fire. This framework includes an algorithm inspired by swarm intelligence that is based on Ant Colony Optimization (ACO) and Dynamic Programming. The main goal of this algorithm is to analyse and find the shortest paths between the diverse regions of a woodland, and prioritize the extinction tasks over the diverse active seats of fire. The framework is based on a theoretical model that allows us to represent the main elements of the environment in which fire spreads. The spread of the wildfire is represented by a model based on cellular automata. The implemented tool provides a visual functionality to model landscapes in detail.