Towards Data Assimilation in Level-Set Wildfire Models Using Bayesian Filtering

TL;DR

This paper extends level-set wildfire models with Bayesian filtering to enable real-time data assimilation and uncertainty quantification, improving fire prediction accuracy and aiding decision-making.

Contribution

It introduces a novel integration of Bayesian filtering with level-set methods for wildfire modeling, allowing for real-time data assimilation and uncertainty estimation.

Findings

Successful demonstration on controlled fire data

Enhanced fire front prediction accuracy

Quantified uncertainty in fire spread estimates

Abstract

The level-set method is a prominent approach to modelling the evolution of a fire over time based on a characterised rate of spread. It however does not provide a direct means for assimilating new data and quantifying uncertainty. Fire front predictions can be more accurate and agile if the models are able to assimilate data in real time. Furthermore, uncertainty estimation of the location and spread of the fire is critical for decision making. Using Bayesian filtering approaches, we extend the level-set method to allow for data assimilation and uncertainty quantification. We demonstrate these approaches on data from a controlled fire.