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In recent years, the increasing threat of devastating wildfires hasunderscored the need for effective prescribed fire management. Process-basedcomputer simulations have traditionally been employed to plan prescribed firesfor wildfire prevention. However, even simplified process models like QUIC-Fireare too compute-intensive to be used for real-time decision-making, especiallywhen weather conditions change rapidly. Traditional ML methods used for firemodeling offer computational speedup but struggle with physically inconsistentpredictions, biased predictions due to class imbalance, biased estimates forfire spread metrics (e.g., burned area, rate of spread), and generalizabilityin out-of-distribution wind conditions. This paper introduces a novel machinelearning (ML) framework that enables rapid emulation of prescribed fires whileaddressing these concerns. By incorporating domain knowledge, the proposedmethod helps reduce physical inconsistencies in fuel density estimates indata-scarce scenarios. To overcome the majority class bias in predictions, weleverage pre-existing source domain data to augment training data and learn thespread of fire more effectively. Finally, we overcome the problem of biasedestimation of fire spread metrics by incorporating a hierarchical modelingstructure to capture the interdependence in fuel density and burned area.Notably, improvement in fire metric (e.g., burned area) estimates offered byour framework makes it useful for fire managers, who often rely on these firemetric estimates to make decisions about prescribed burn management.Furthermore, our framework exhibits better generalization capabilities than theother ML-based fire modeling methods across diverse wind conditions andignition patterns.

Prescribed Fire Modeling using Knowledge-Guided Machine Learning for Land Management