Explainable machine learning approaches for understanding fire outcomes

Prescribed fires are an important part of forest stewardship in North America, understanding prescribed burn behavior is important because if done incorrectly can result in unintended burned land as well as harm to humans and the environment. We looked at ensemble datasets from QUIC-Fire, a fire-atmospheric modeling tool(1), and compared various machine learning models' effectiveness at predicting outcome variables, such as area burned inside and outside the control boundary, and if the fire behavior was safe or unsafe. It was found that out of the tested machine learning models random forest performed best at predicting all three predictor variables of interest.