  Parametric model order reduction techniques often struggle to accurately
represent transport-dominated phenomena due to a slowly decaying Kolmogorov
n-width. To address this challenge, we propose a non-intrusive, data-driven
methodology that combines the shifted proper orthogonal decomposition (POD)
with deep learning. Specifically, the shifted POD technique is utilized to
derive a high-fidelity, low-dimensional model of the flow, which is
subsequently utilized as input to a deep learning framework to forecast the
flow dynamics under various temporal and parameter conditions. The efficacy of
the proposed approach is demonstrated through the analysis of one- and
two-dimensional wildland fire models with varying reaction rates, and its
performance is evaluated using multiple error measures. The results indicate
that the proposed approach yields highly accurate results within the percent
range, while also enabling rapid prediction of system states within seconds.
