Data-driven modeling of wildfire spread with stochastic cellular automata and latent spatio-temporal dynamics

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.

• Publication year

  2023

• Venue

  Spatial Statistics

• Publication date

  2023-06-05

• Fields of study

  Mathematics, Computer Science, Environmental Science

• Identifiers
  DOI 10.1016/j.spasta.2023.100794arXiv 2306.03214
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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