Wildfire Smoke and Air Quality: How Machine Learning Can Guide Forest Management

TL;DR

This paper demonstrates how machine learning techniques like spectral clustering and manifold learning can help forest managers identify safe burning practices to reduce wildfire risk and minimize toxic smoke exposure.

Contribution

It introduces machine learning methods as tools for differentiating smoke types, aiding in safer and more effective forest fire management strategies.

Findings

Machine learning can classify smoke types effectively.

Spectral clustering and manifold learning provide interpretable representations.

Tools can guide safer prescribed burns to reduce wildfire risk.

Abstract

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.