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.

中文翻译：

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利用随机元胞自动机和潜在时空动力学对野火蔓延进行数据驱动建模

我们提出了一种贝叶斯随机细胞自动机建模方法，通过不确定性量化来模拟野火的蔓延。该模型考虑了动态邻域结构，允许邻域状态告知多状态分类模型中的转移概率。通过使用通过空间基函数链接到原始空间域的时间演化的潜在时空动态过程来捕获附加空间信息。贝叶斯构造允许对与每个预测的火灾状态相关的不确定性进行量化。该方法适用于高度仪器化控制的烧伤。