Explainable machine learning approaches for understanding fire outcomes

Megan Booher; James Ahrens; Ayan Biswas

doi:10.1117/12.2677931

4 October 2023 Explainable machine learning approaches for understanding fire outcomes

Megan Booher, James Ahrens, Ayan Biswas

Proceedings Volume 12675, Applications of Machine Learning 2023; 1267515 (2023) https://doi.org/10.1117/12.2677931
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Conference Poster

Abstract

Prescribed fires are an important part of forest stewardship in Western 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, 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.

Citation Download Citation

Megan Booher, James Ahrens, and Ayan Biswas "Explainable machine learning approaches for understanding fire outcomes", Proc. SPIE 12675, Applications of Machine Learning 2023, 1267515 (4 October 2023); https://doi.org/10.1117/12.2677931

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