Bayesian Optimisation for Active Monitoring of Air Pollution

Autor:	Sigrid Passano Hellan, Christopher G. Lucas, Nigel H. Goddard
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning Physics - Atmospheric and Oceanic Physics Atmospheric and Oceanic Physics (physics.ao-ph) FOS: Physical sciences ComputerApplications_COMPUTERSINOTHERSYSTEMS General Medicine Machine Learning (cs.LG)
DOI:	10.48550/arxiv.2202.07595
Popis:	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. Comment: To be presented at AAAI 2022 in the Special Track on AI for Social Impact
Databáze:	OpenAIRE
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