Abstract

Air pollution is one of the most important causes of mortality in the world. Monitoring air pollution is useful to learn more about the link between health and pollutants, and to identify areas for intervention. Such monitoring is expensive, so it is important to place sensors as efficiently as possible. Bayesian optimisation has proven useful in choosing sensor locations, but typically relies on kernel functions that neglect the statistical structure of air pollution, such as the tendency of pollution to propagate in the prevailing wind direction. We describe two new wind-informed kernels and investigate their advantage for the task of actively learning locations of maximum pollution using Bayesian optimisation.
Original languageEnglish
Number of pages8
Publication statusAccepted/In press - 31 Oct 2020
EventAI for Earth Sciences at NeurIPS 2020 - Virtual
Duration: 12 Dec 202012 Dec 2020
https://ai4earthscience.github.io/neurips-2020-workshop/

Conference

ConferenceAI for Earth Sciences at NeurIPS 2020
Abbreviated titleAI4Earth 2020
CityVirtual
Period12/12/2012/12/20
Internet address

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