Theoretical Computer Science - Special issue: cellular automata
Future Generation Computer Systems - Special issue on cellular automata: promise in computational science
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
A macroscopic collisional model for debris-flows simulation
Environmental Modelling & Software
Fundamenta Informaticae - Membrane Computing
Integrated simulation and optimization for wildfire containment
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Data assimilation using sequential monte carlo methods in wildfire spread simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Accelerating wildfire susceptibility mapping through GPGPU
Journal of Parallel and Distributed Computing
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Cell-based methods for simulating wildfires can be computationally more efficient than techniques based on the fire perimeter expansion. In spite of this, their success has been limited by the distortions that plague the simulated shapes. This article presents a novel algorithm for wildfire simulation through Cellular Automata (CA), which is able to effectively mitigate the problem of distorted fire shapes. Such a result is obtained allowing spread directions that are not constrained to the few angles imposed by the lattice of cells and the neighborhood size. The characteristics of the proposed algorithm are empirically investigated under homogeneous conditions through some comparisons with the outcomes of a typical CA-based simulator. Also, using two significant heterogeneous landscapes, a comparison with the vector-based simulator FARSITE is discussed. According to the results of this study, the proposed approach performs significantly better, in terms of accuracy, than the CA taken as reference. In addition, at a far less computational cost, it provides burned regions that are equivalent, for practical purposes, to those given by FARSITE.