  In recent years, the increasing threat of devastating wildfires has
underscored the need for effective prescribed fire management. Process-based
computer simulations have traditionally been employed to plan prescribed fires
for wildfire prevention. However, even simplified process models like QUIC-Fire
are too compute-intensive to be used for real-time decision-making, especially
when weather conditions change rapidly. Traditional ML methods used for fire
modeling offer computational speedup but struggle with physically inconsistent
predictions, biased predictions due to class imbalance, biased estimates for
fire spread metrics (e.g., burned area, rate of spread), and generalizability
in out-of-distribution wind conditions. This paper introduces a novel machine
learning (ML) framework that enables rapid emulation of prescribed fires while
addressing these concerns. By incorporating domain knowledge, the proposed
method helps reduce physical inconsistencies in fuel density estimates in
data-scarce scenarios. To overcome the majority class bias in predictions, we
leverage pre-existing source domain data to augment training data and learn the
spread of fire more effectively. Finally, we overcome the problem of biased
estimation of fire spread metrics by incorporating a hierarchical modeling
structure to capture the interdependence in fuel density and burned area.
Notably, improvement in fire metric (e.g., burned area) estimates offered by
our framework makes it useful for fire managers, who often rely on these fire
metric estimates to make decisions about prescribed burn management.
Furthermore, our framework exhibits better generalization capabilities than the
other ML-based fire modeling methods across diverse wind conditions and
ignition patterns.
