Models that predict wildfire rate of spread (ROS) play an important role in decision-making during firefighting operations, including fire crew placement and timing of community evacuations. Here, we use a large set of remotely sensed wildfire observations, and explanatory data (focusing on weather), to demonstrate a Bayesian probabilistic ROS modelling approach. Our approach has two major advantages: (1) Using actual wildfire observations, instead of controlled fire observations, makes models developed well-suited to wildfire prediction; (2) Bayesian modelling accounts for the complex nature of wildfire spread by explicitly considering uncertainty in the data to produce probabilistic ROS predictions. We show that highly informative probabilistic predictions can be made from a simple Bayesian model containing wind speed, relative humidity and soil moisture. We provide current operational context to our work by calculating predictions from widely used deterministic ROS models in Australia.

预测野火蔓延率 (ROS) 的模型在消防行动的决策中发挥着重要作用，包括消防人员的安置和社区疏散的时间安排。在这里，我们使用大量遥感野火观测和解释性数据（侧重于天气）来演示贝叶斯概率 ROS 建模方法。我们的方法有两个主要优点：（1）使用实际的野火观察，而不是受控的火灾观察，使模型开发非常适合野火预测；(2) 贝叶斯建模通过明确考虑数据中的不确定性来产生概率性 ROS 预测来解释野火蔓延的复杂性。我们表明，可以从包含风速的简单贝叶斯模型中进行信息量丰富的概率预测，相对湿度和土壤湿度。我们通过计算澳大利亚广泛使用的确定性 ROS 模型的预测，为我们的工作提供当前的操作环境。