Accelerating optimization of input parameters in wildland fire simulation

Abstract

Fire propagation simulation is seen as a challenging problem in the area of simulation, due to the complexity of the physical models involved, the need for a great amount of computation and the difficulties of providing accurate input parameters. Input parameters appear as one of the major sources of deviation between predicted results and real-fire propagation. Evolutionary algorithms have been used to optimize the input parameters. However, such optimization techniques must be carried out during real-time operation and, therefore, certain methods must be applied to accelerate the optimization process. These methods take advantage of the computational power offered by distributed systems.