  Fire propagation is a major concern in the world in general and in
Argentinian northwestern Patagonia in particular where every year hundreds of
hectares are affected by both natural and anthropogenic forest fires. We
developed an efficient cellular automata model in Graphic Processing Units
(GPUs) to simulate fire propagation. The graphical advantages of GPUs were
exploded by overlapping wind direction maps, as well as vegetation, slope and
aspect maps, taking into account relevant landscape characteristics for fire
propagation. Stochastic propagation was performed with a probability model that
depends on aspect, slope, wind direction and vegetation type. Implementing a
genetic algorithm search strategy we show, using simulated fires, that we
recover the five parameter values that characterize fire propagation. The
efficiency of the fire simulation procedure allowed us to also estimate the
fire ignition point when it is unknown as well as its associated uncertainty,
making this approach suitable for the analysis of fire spread based on maps of
burned areas without knowing the point of origin of the fires or how they
spread.
