My research focuses on exploring the stochastic and natural variability of various sources, integrating the uncertainty associated with these factors into wildfire spread predictions. Utilizing stochastic mathematical models enables the tracking of two-dimensional fire propagation over time. This capability proves beneficial for decision-makers, allowing them to assess risks and effectively manage situations during wildfire events.

PhD project addresses the important problem of incorporating environmental uncertainty in bushfire propagation modelling.  To faithfully capture a range of uncertainties related to environmental inputs (in particular wind velocity) in bushfire propagation models, it is important to incorporate the stochastic nature of these inputs. In this research project, wind is modelled using stochastic processes in an attempt to achieve more reliable bushfire spread predictions compared to other probabilistic approaches. Two-dimensional bushfire propagation can be modelled using the level set method, and environmental uncertainty can be incorporated either directly into the bushfire spread model or by extending the propagation model to a stochastic level set approach. Stochastic bushfire simulations are compared to experimental fire spread data collected as part of broader international collaboration.