论文标题
贝叶斯优化用于主动监测空气污染
Bayesian Optimisation for Active Monitoring of Air Pollution
论文作者
论文摘要
空气污染是全球死亡率的主要原因之一，每年导致数百万人死亡。有效的监控对于衡量暴露和执行法律限制非常重要。新的低成本传感器可以在数量和更多样化的位置部署，从而激发了有效的自动放置问题。先前的工作表明，贝叶斯优化是一种适当的方法，但仅视为卫星数据集，并且在所有高度上汇总了数据集。人类呼吸是地面污染，最重要。我们使用分层模型改进了这些结果，并在伦敦的城市污染数据上评估了我们的模型，以表明贝叶斯优化可以成功地应用于该问题。
Air pollution is one of the leading causes of mortality globally, resulting in millions of deaths each year. Efficient monitoring is important to measure exposure and enforce legal limits. New low-cost sensors can be deployed in greater numbers and in more varied locations, motivating the problem of efficient automated placement. Previous work suggests Bayesian optimisation is an appropriate method, but only considered a satellite data set, with data aggregated over all altitudes. It is ground-level pollution, that humans breathe, which matters most. We improve on those results using hierarchical models and evaluate our models on urban pollution data in London to show that Bayesian optimisation can be successfully applied to the problem.
