Wildfire propagation is a highly stochastic process where small changes in environmental conditions (such as wind speed and direction) can lead to large changes in observed behaviour. A traditional approach to quantify uncertainty in fire-front progression is to generate probability maps via ensembles of simulations. However, use of ensembles is typically computationally expensive, which can limit the scope of uncertainty analysis. To address this, we explore the use of a spatio-temporal neural-based modelling approach to directly estimate the likelihood of fire propagation given uncertainty in input parameters. The uncertainty is represented by deliberately perturbing the input weather forecast during model training. The computational load is concentrated in the model training process, which allows larger probability spaces to be explored during deployment. Empirical evaluations indicate that the proposed model achieves comparable fire boundaries to those produced by the traditional SPARK simulation platform, with an overall Jaccard index (similarity score) of 67.4% on a set of 35 simulated fires. When compared to a related neural model (emulator) which was employed to generate probability maps via ensembles of emulated fires, the proposed approach produces competitive Jaccard similarity scores while being approximately an order of magnitude faster.

翻译：野火传播是一个高度随机的过程，环境条件（如风速和风向）的微小变化可能导致观测行为的巨大改变。量化火线推进不确定性的传统方法是通过模拟集成生成概率图。然而，集成方法通常计算成本高昂，这限制了不确定性分析的范围。为解决这一问题，我们探索使用基于时空神经网络的建模方法，直接估计在输入参数不确定性下野火传播的可能性。通过在模型训练过程中故意扰动输入天气预报来表征不确定性。计算负载集中在模型训练阶段，从而在部署时能够探索更大的概率空间。实证评估表明，所提模型在35场模拟火灾上实现了与传统SPARK仿真平台相媲美的火灾边界，总体杰卡德指数（相似度得分）达67.4%。与通过模拟火灾集成生成概率图的相关神经模型（仿真器）相比，所提方法在获得具有竞争力的杰卡德相似度得分的同时，速度提升约一个数量级。