2023/06/05 by Nicholas Grieshop, Christopher K. Wikle, Grieshop, Nicholas +1 · 1 citation
Computer Science · #Applications (stat.AP) #Cellular Automata and Applications #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2306.03214
openalex publication_date 2023/06/05 · openalex created_date 2023/06/09 · openalex updated_date 2026/07/28
We propose a Bayesian stochastic cellular automata modeling approach to model the spread of wildfires with uncertainty quantification. The model considers a dynamic neighborhood structure that allows neighbor states to inform transition probabilities in a multistate categorical model. Additional spatial information is captured by the use of a temporally evolving latent spatio-temporal dynamic process linked to the original spatial domain by spatial basis functions. The Bayesian construction allows for uncertainty quantification associated with each of the predicted fire states. The approach is applied to a heavily instrumented controlled burn.