Forest Fire Simulation

Cellular automata represent a mathematical modeling approach consisting of a regular grid of cells, each possessing a finite set of possible states. Each cell transitions between states based on predefined rules that consider the states of neighboring cells. This computational framework finds applications across diverse domains including physics, biology, economics, and sociology. In implementation, cellular automata typically employ a grid data structure where each cell's state update follows conditional logic (e.g., if-else statements or switch cases) based on Moore or von Neumann neighborhoods. Key algorithmic components include: state initialization functions, neighborhood detection methods, and synchronous/asynchronous update mechanisms. For forest fire modeling specifically, common state transitions might include: healthy tree → burning tree → ash, with ignition probabilities based on adjacent burning cells and environmental factors. Beyond scientific simulations, cellular automata serve creative purposes in generative art and music composition through their capacity to produce intricate visual patterns and auditory sequences. The core implementation often involves nested loops for grid traversal, state buffers for simultaneous updates, and visualization routines for real-time pattern observation.