SDS Seminar Series – Myungsoo Yoo, University of Texas at Austin

Nov

2024

2:00 pm – 3:00 pm

Cost: Free

In Person (view details)

Dynamic Spatio-Temporal Model Integrating Physics for Fire Front Propagation

Description

The Fall 2024 SDS Seminar Series continues on November 8th from 2:00 p.m. to 3:00 p.m. with Dr. Myungsoo Yoo (Postdoctoral Fellow, Department of Statistics and Data Sciences, University of Texas at Austin). This event is in-person in CBA 4.348.

Title: Dynamic Spatio-Temporal Model Integrating Physics for Fire Front Propagation

Abstract: Intense wildfires impact nature, humans, and society, causing catastrophic damage to property, ecosystems, and loss of life. Forecasting wildfire front propagation is essential to support firefighting efforts and plan evacuations. The level set method has been widely used to analyze changes in surfaces, shapes, and boundaries. In particular, the signed distance function used in level set methods can effectively represent complex boundaries and their temporal changes. While substantial literature exists on applying the level set method to wildfire modeling, these implementations typically rely on heavily parameterized formulas for the rate of spread. Moreover, these implementations have not typically considered uncertainty quantification or incorporated data-driven learning. To address these, this talk will introduce two mechanistic approaches to modeling the evolution of wildfire boundaries, accounting for uncertainty in data and limited knowledge of boundaries. First, an approach using a level set method and low-rank representation within a Bayesian Hierarchical Model will be introduced. Subsequently, a hybrid model that nests an echo state network within a level-set method to accommodate nonlinearity will be presented. We demonstrate the effectiveness of our methods on the fire front boundary evolution of two mega wildfires: the 2017-2018 Thomas fire and Haypress fire.