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:野火可能是毁灭性的,对财产、生态系统的破坏和生命的丧失造成重大破坏。预测野火边界的演变对于实时野火管理至关重要。为此,野火文献大量关注水平设定方法,这实际上代表了复杂的边界及其随时间的变化。然而,这些方法大多依赖一个高度分立的分布公式,而没有考虑到预测的不确定性。大型野火和各种环境条件在利益领域(例如,不同的土地覆盖、火灾引发的风)的迅速演变需要一种模型,以便能够有效地以数据驱动的方式学习火灾蔓延,并能够量化不确定性。在这里,我们提出了一个新型混合模型,将一个回声状态网络嵌套起来,在一个以物理为基础的水平设定模型框架内学习非线性瞬间变化速度(正常方向的速度)。这个模型是计算有效的,包括经校准的不确定性量化。我们用模拟和两个真实的数据集(海布雷斯和托马斯)展示了模型的预测性能,该模型始于201717年的加利福尼亚州。