A Bayesian spatio-temporal level set dynamic model and application to fire front propagation

Abstract

Intense wildfires impact nature, humans, and society, causing catastrophic damage to property and the ecosystem as well as the loss of life. Forecasting wildfire front propagation and understanding the behavior of wildfire propagation within a formal uncertainty quantification framework are essential in order to support fire fighting efforts and plan evacuations. The level set method has been widely used to analyze the change in surfaces, shapes, and boundaries. In particular, a signed distance function used in level set methods can readily be interpreted to represent complicated boundaries and their changes in time. While there is substantial literature on the level set method in wildfire applications, these implementations have relied on a heavily-parameterized formula for the rate of spread. These implementations have not typically considered uncertainty quantification, incorporated data-driven learning, nor summarized the effect of the environmental covariates. Here we present a Bayesian spatio-temporal dynamic model, based on level sets, which can be utilized for inference and forecasting the boundary of interest in the presence of uncertain data and lack of knowledge about the boundary velocity. The methodology relies on both a mechanistically-motivated dynamic model for level sets and a stochastic spatio-temporal dynamic model for the front velocity. We show the effectiveness of our method via simulation and with forecasting the fire front boundary evolution of two classic California megafires—the 2017–2018 Thomas fire and the 2017 Haypress fire.