Wildfire Smoke and Air Quality: How Machine Learning Can Guide Forest Management (Proposals Track) Spotlight

Lorenzo Tomaselli (Carnegie Mellon University); Coty Jen (Carnegie Mellon University); Ann Lee (Carnegie Mellon University)

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Disaster Management and Relief Interpretable ML Unsupervised & Semi-Supervised Learning Forests


Prescribed burns are currently the most effective method of reducing the risk of widespread wildfires, but a largely missing component in forest management is knowing which fuels one can safely burn to minimize exposure to toxic smoke. Here we show how machine learning, such as spectral clustering and manifold learning, can provide interpretable representations and powerful tools for differentiating between smoke types, hence providing forest managers with vital information on effective strategies to reduce climate-induced wildfires while minimizing production of harmful smoke.

Recorded Talk (direct link)