Wildfires can be devastating, causing significant damage to property, ecosystem disruption, and loss of life. Forecasting the evolution of wildfire boundaries is essential to real-time wildfire management. To this end, substantial attention in the wildifre literature has focused on the level set method, which effectively represents complicated boundaries and their change over time. Nevertheless, most of these approaches rely on a heavily-parameterized formulas for spread and fail to account for the uncertainty in the forecast. The rapid evolution of large wildfires and inhomogeneous environmental conditions across the domain of interest (e.g., varying land cover, fire-induced winds) give rise to a need for a model that enables efficient data-driven learning of fire spread and allows uncertainty quantification. Here, we present a novel hybrid model that nests an echo state network to learn nonlinear spatio-temporal evolving velocities (speed in the normal direction) within a physically-based level set model framework. This model is computationally efficient and includes calibrated uncertainty quantification. We show the forecasting performance of our model with simulations and two real data sets - the Haybress and Thomas megafires that started in California (USA) in 2017.

翻译：野火可能带来毁灭性后果，导致财产重大损失、生态系统破坏和人员伤亡。预测野火边界的演变对于实时野火管理至关重要。为此，野火研究文献中大量关注水平集方法，该方法能有效表示复杂边界及其随时间的变化。然而，大多数这类方法依赖高度参数化的传播公式，且未能考虑预测中的不确定性。大型野火的快速演变以及感兴趣区域内不均匀的环境条件（例如，不同土地覆盖类型、火场诱导风）催生了对既能实现高效数据驱动的火势传播学习、又能进行不确定性量化的模型的需求。本文提出一种新型混合模型，该模型在基于物理的水平集模型框架内嵌套回声状态网络，用于学习非线性时空演化速度（法向方向上的速率）。该模型计算高效，并包含校准后的不确定性量化。我们通过模拟实验和两个真实数据集——2017年起源于美国加利福尼亚州的Haybress和Thomas特大野火——展示了模型的预测性能。