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.
|Number of pages||8|
|Publication status||Accepted/In press - 31 Oct 2020|
|Event||AI for Earth Sciences at NeurIPS 2020 - Virtual|
Duration: 12 Dec 2020 → 12 Dec 2020
|Conference||AI for Earth Sciences at NeurIPS 2020|
|Abbreviated title||AI4Earth 2020|
|Period||12/12/20 → 12/12/20|