  As the climate changes, the severity of wildland fires is expected to worsen.
Models that accurately capture fire propagation dynamics greatly help efforts
for understanding, responding to and mitigating the damages caused by these
fires. Machine learning techniques provide a potential approach for developing
such models. The objective of this study is to evaluate the feasibility of
using a Convolutional Long Short-Term Memory (ConvLSTM) recurrent neural
network to model the dynamics of wildland fire propagation. The machine
learning model is trained on simulated wildfire data generated by a
mathematical analogue model. Three simulated datasets are analyzed, each with
increasing degrees of complexity. The simplest dataset includes a constant wind
direction as a single confounding factor, whereas the most complex dataset
includes dynamic wind, complex terrain, spatially varying moisture content and
heterogenous vegetation density distributions. We examine how effective the
ConvLSTM can learn the fire-spread dynamics over consecutive time steps. It is
shown that ConvLSTMs can capture local fire transmission events, as well as the
overall fire dynamics, such as the rate at which the fire spreads. Finally, we
demonstrate that ConvLSTMs outperform other network architectures that have
previously been used to model similar wildland fire dynamics.
