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Modeling and Simulating California Wildfires through Fuzzy Cellular Automata
Presentation, Technology, Modeling
Johns Hopkins University - Whiting School of Engineering
Independently researched applications of computational model for wildfire modeling and simulation to predict chaotic spread.
Wildfires are chaotic phenomena responsible for causing major economic damage. Due its chaotic nature it's been the interest of various mathematical/computational modeling efforts. Cellular automata is most commonly used due to its light complexity. Cellular automata is the most effective due to its dependence on current states and simultaneous update rules.
Different cellular automata models are explored through this project - traditional, tracking, and fuzzy tracking. The logic behind fuzzy tracking is recognizing that multiple parameters impact the likelihood of a fire spreading to a certain spot. Fuzzy logic further allows us to reduce the state space while considering all the parameters.
Since fuzzy logic is used the hyper parameters were tuned based on analyzing the stability of the model. They were not tuned through ML training.