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.

翻译：我们提出了一种贝叶斯随机元胞自动机建模方法，用于在量化不确定性的条件下模拟野火蔓延。该模型采用动态邻域结构，使得邻域状态能够为多类别分类模型中的转移概率提供信息。通过使用随时间演化的潜在时空动态过程，并借助空间基函数将其与原始空间域相关联，进一步捕获了额外的空间信息。贝叶斯构造方式使得每个预测火势状态均能关联不确定性量化。该方法已应用于一次高度仪器化的可控燃烧实验。