A Bayesian Spatio-Temporal Level Set Dynamic Model and Application to Fire Front Propagation

TL;DR

This paper introduces a Bayesian spatio-temporal level set model for wildfire front prediction, incorporating uncertainty quantification and data-driven learning, demonstrated on major California fires.

Contribution

It develops a novel Bayesian level set framework that models wildfire front dynamics with uncertainty quantification, improving upon traditional parameterized methods.

Findings

Effective in simulating fire front evolution

Accurate forecasting of real wildfire boundaries

Incorporates uncertainty in predictions

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 is 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 or incorporated data-driven learning. Here, we present a Bayesian spatio-temporal dynamic model based on level sets, which can be utilized for 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.