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The level-set method is a prominent approach to modelling the evolution of afire over time based on a characterised rate of spread. It however does notprovide a direct means for assimilating new data and quantifying uncertainty.Fire front predictions can be more accurate and agile if the models are able toassimilate data in real time. Furthermore, uncertainty estimation of thelocation and spread of the fire is critical for decision making. Using Bayesianfiltering approaches, we extend the level-set method to allow for dataassimilation and uncertainty quantification. We demonstrate these approaches ondata from a controlled fire.

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