Revisão Bibliográfica: Autômatos Celulares
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- Fonte: http://madeira.cc.hokudai.ac.jp/RD/takai/automa.html
- Title: Burn: A Simulation of Forest Propagation
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Fonte: http://madeira.cc.hokudai.ac.jp/RD/takai/automa.html
Voltar 3.3) Modelagens Ambientais Cellular automata, while exceedingly simple to describe, often exhibit surprisingly complex behavior. For example, empirical studies by Wolfram and others have shown that even the simplest of cellular automata can exhibit such emergent traits as self-organization, periodic stability, indefinite growth, irregular contraction, and self-replication. It is this phenomena of emergent life like behavior which have made cellular automata so popular in artificial life research - both as a metaphor and a tool for implementation. However, all problems are not equal - some provide a problem space better suited for a cellular automata approach than other. for example, the problem of ecological modeling has the potential to take full advantage of the strengths of cellular automata. Consider a cellular automaton model of a landscape consisting of fixed arrays where each cell represents an area of the land surface. The number of cells would fix the scale. And a cell's state could correspond to any number of environmental factors for example, population, elevation or terrain. The neighborhood functions could simulate ecological processes such as seed dispersal, epidemic spread or even forest fire propagation. time could be modeled by each iterative update where the scale depends on the particular process under simulation. Green points out that such an approach has many advantages. The cellular nature of the model makes the approach compatible with the pixel-based satellite imagery or with quadratic-based field observations. Furthermore, it allows processes that involve movement through space and time to be modeled intuitively and easily. In fact, cellular automata models have been successfully implemented in a variety of ecological modeling applications including starfish outbreaks, spread of disease, and forest dynamics. Voltar 3.3.1) Fogos Florestais While several computer simulations of fire propagation currently exist, none provide a comprehensive model designed for use with multiple terrains of a heterogeneous fuel bed. For example, some are designed for use with general terrains but lack an accurate fire model. While others have a sophisticated simulation that succeeds only with specific predetermined terrains. However, many of the models do have a common element; they base their simulations, to some degree, on a set of fire-spread equations designed by Richard Rothermel in 1983. What follows is a general description of a simple fire spread model, which is based on Rothermel's equations. Although the complete set of formulas have not been included, all necessary calculations can be done via direct computation or monograms. In general, a fire prediction process has three elementary components: obtaining input, calculating fire behavior, and interpreting output. However, due to the large number of relatively uninteresting, detailed equations required to relate the calculation procedure, in this paper, merely describe the most important of those equations. Bibliografia: Fonte: http://citeseer.nj.nec.com Referências Principais: Title: Burn: A Simulation of Forest PropagationAuthor: Marshall Veach Abstract: A project is described which designed, implemented and evaluated a simulation of forest fire propagation as a cellular automaton in a parallel environment. Fire models developed by Richard Rothermel were used as the basis for propagation behavior. Furthermore, the simulation was developed to support a variety of applications including fire propagation prediction and the evaluation of fire fighting effectiveness. 1 Artificial Life, Cellular Automata & Ecological Modeling: An Introduction 1.1 Artificial Life Artificial life is the study of artificial systems that exhibit behavior characteristic of natural living systems [4]. 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