Bayesian Optimisation for Active Monitoring of Air Pollution
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866914760599011328 |
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| author | Hellan, Sigrid Passano Lucas, Christopher G. Goddard, Nigel H. |
| author_facet | Hellan, Sigrid Passano Lucas, Christopher G. Goddard, Nigel H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2202_07595 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Bayesian Optimisation for Active Monitoring of Air Pollution Hellan, Sigrid Passano Lucas, Christopher G. Goddard, Nigel H. Machine Learning Atmospheric and Oceanic Physics 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. |
| title | Bayesian Optimisation for Active Monitoring of Air Pollution |
| topic | Machine Learning Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2202.07595 |