Top Menu

Dissertation Defense – Cong Zhang – June 21 – 10:00am

Committee: Dr. Arnaud Trouve, Chair/Advisor
Dr. Melanie Rochoux
Dr. Michael Gollner
Dr. Andre Marshall
Dr. Shapour Azarm
Dr. Kayo Ide, Dean’s Representative

Date/timeJune 21 (Thursday), 10 am

Location: 3106B conference room in JM Patterson building

Abstract: Current wildfire spread simulators lack the ability to provide accurate prediction of the active flame burning areas at regional scales due to two main challenges: a modeling challenge associated with providing accurate mathematical representations of the multi-physics multi-scale processes that induce the fire dynamics, and a data challenge associated with providing accurate estimates of the initial fire position and the physical parameters that are required by the fire spread models. A promising approach to overcome these limitations is data assimilation: data assimilation aims at integrating available observations into the fire spread simulator, while accounting for their respective uncertainties, in order to infer a more accurate estimate of the fire front position and to produce a more reliable forecast of the wildfire behavior.

The main objective of the present study is to design and evaluate suitable algorithms for regional-scale wildfire spread simulations, which are able to properly handle the variations in wildfire spread due to the significant spatial heterogeneity in the model inputs and to the temporal changes in the wildfire behavior. First we developed a grid-based spatialized parameter estimation approach where the estimation targets are the spatially-varying input model parameters. Then we proposed an efficient and robust method to compute the discrepancy between the observed and simulated fire fronts, which is based on a front shape similarity measure inspired from image processing theory. The new method is demonstrated in the context of Luenberger observer-based state estimation strategy. Finally we developed a joint state-parameter estimation method where we estimate both model state and model parameters simultaneously in order to retrieve more accurate physical values of model parameters and achieve a better forecast performance in terms of fire front positions. All these efforts aim at designing algorithmic solutions to overcome the difficulties associated with spatially-varying environmental conditions and potentially complex fireline shapes and topologies. It paves the way towards real-time monitoring and forecasting of wildfire dynamics at regional scales.