  The increasing incidence and severity of wildfires underscores the necessity
of accurately predicting their behavior. While high-fidelity models derived
from first principles offer physical accuracy, they are too computationally
expensive for use in real-time fire response. Low-fidelity models sacrifice
some physical accuracy and generalizability via the integration of empirical
measurements, but enable real-time simulations for operational use in fire
response. Machine learning techniques offer the ability to bridge these
objectives by learning first-principles physics while achieving computational
speedup. While deep learning approaches have demonstrated the ability to
predict wildfire propagation over large time periods, time-resolved fire-spread
predictions are needed for active fire management. In this work, we evaluate
the ability of deep learning approaches in accurately modeling the
time-resolved dynamics of wildfires. We use an autoregressive process in which
a convolutional recurrent deep learning model makes predictions that propagate
a wildfire over 15 minute increments. We demonstrate the model in application
to three simulated datasets of increasing complexity, containing both field
fires with homogeneous fuel distribution as well as real-world topologies
sampled from the California region of the United States. We show that even
after 100 autoregressive predictions representing more than 24 hours of
simulated fire spread, the resulting models generate stable and realistic
propagation dynamics, achieving a Jaccard score between 0.89 and 0.94 when
predicting the resulting fire scar.
