Sharma, Balaji (University of Cincinnati), Kumar, Manish (University of Toledo), Cohen, Kelly (University of Cincinnati)

Spatio-Temporal Estimation of Wildfire Growth

Scheduled for presentation during the Contributed session "Flow and Thermal Systems" (WeAT4), Wednesday, October 23, 2013, 11:35−11:55, Paul Brest West

6th Annual Dynamic Systems and Control Conference, October 21-23, 2020, Stanford University, Munger Center, Palo Alto, CA

This information is tentative and subject to change. Compiled on April 25, 2024

Abstract

This work presents a methodology for real-time estimation of wildland fire growth, utilizing a fire growth model based on a set of partial differential equations for prediction, and harnessing concepts of space-time Kalman filtering and Proper Orthogonal Decomposition techniques towards low dimensional estimation of potentially large spatio-temporal states. The estimation framework is discussed in its criticality towards potential applications such as forest fire surveillance with unmanned systems equipped with onboard sensor suites. The effectiveness of the estimation process is evaluated numerically over fire growth data simulated using a well-established fire growth model described by coupled partial differential equations. The methodology is shown to be fairly accurate in estimating spatio-temporal process states through noise-ridden measurements for real-time deployability.