An optimal Cellular Automata algorithm for simulating wildfire spread

Abstract

Raster-based methods for simulating wildfire spread are computationally more efficient than vector-based approaches. In spite of this, their success has been limited by the distortions that affect the fire shapes. This work presents a Cellular Automata (CA) approach that is able to mitigate the problem of distorted fire shapes thanks to a redefinition of the spread velocity, where the equations generally used in vector-based approaches are modified by means of some correction factors. A numerical optimization approach is used to find the optimal values for the correction factors. The results are compared to the ones given by two Cellular Automata simulators from the literature under homogeneous conditions. According to this work, the proposed approach provides better results, in terms of accuracy, at a comparable computational cost. The proposed approach has then been compared to Farsite, a vector-based fire-spread simulator, under realistic slope and wind conditions, producing equivalent results in a reduced computational time.