Wildfire Smoke and Air Quality: How Machine Learning Can Guide Forest Management
This addresses forest management challenges by offering data-driven insights to reduce climate-induced wildfires while limiting harmful smoke production.
The paper tackles the problem of minimizing toxic smoke exposure from prescribed burns by using machine learning (spectral clustering and manifold learning) to differentiate between smoke types, providing forest managers with interpretable tools to guide effective wildfire reduction strategies.
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.