  Wildfire propagation is a highly stochastic process where small changes in
environmental conditions (such as wind speed and direction) can lead to large
changes in observed behaviour. A traditional approach to quantify uncertainty
in fire-front progression is to generate probability maps via ensembles of
simulations. However, use of ensembles is typically computationally expensive,
which can limit the scope of uncertainty analysis. To address this, we explore
the use of a spatio-temporal neural-based modelling approach to directly
estimate the likelihood of fire propagation given uncertainty in input
parameters. The uncertainty is represented by deliberately perturbing the input
weather forecast during model training. The computational load is concentrated
in the model training process, which allows larger probability spaces to be
explored during deployment. Empirical evaluations indicate that the proposed
model achieves comparable fire boundaries to those produced by the traditional
SPARK simulation platform, with an overall Jaccard index (similarity score) of
67.4% on a set of 35 simulated fires. When compared to a related neural model
(emulator) which was employed to generate probability maps via ensembles of
emulated fires, the proposed approach produces competitive Jaccard similarity
scores while being approximately an order of magnitude faster.
