论文标题
野火烟雾和空气质量：机器学习如何指导森林管理
Wildfire Smoke and Air Quality: How Machine Learning Can Guide Forest Management
论文作者
论文摘要
当前规定的烧伤是降低广泛野火风险的最有效方法，但是森林管理中的大部分缺失的组成部分是知道哪种燃料可以安全燃烧以最大程度地减少暴露于有毒烟雾。在这里，我们展示了机器学习，例如光谱聚类和多种多样学习，可以提供可解释的表示和强大的工具，以区分烟雾类型，从而为森林经理提供有关有效策略的重要信息，以减少气候引起的野火，同时最大程度地减少有害烟雾的产生。
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.
