Machine Learning for a Low-cost Air Pollution Network

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dc.contributor.authorSmith, Michael T.
dc.contributor.authorSsematimba, Joel
dc.contributor.authorÁlvarez, Mauricio A.
dc.contributor.authorBainomugisha, Engineer
dc.date.accessioned2023-01-26T20:25:05Z
dc.date.available2023-01-26T20:25:05Z
dc.date.issued2019
dc.description.abstractData collection in economically constrained countries often necessitates using approximate and biased measurements due to the low-cost of the sensors used. This leads to potentially invalid predictions and poor policies or decision making. This is especially an issue if methods from resource-rich regions are applied without handling these additional constraints. In this paper we show, through the use of an air pollution network example, how using probabilistic machine learning can mitigate some of the technical constraints. Specifically we experiment with modelling the calibration for individual sensors as either distributions or Gaussian processes over time, and discuss the wider issues around the decision process.en_US
dc.identifier.citationSmith, M. T., Ssematimba, J., Alvarez, M. A., & Bainomugisha, E. (2019). Machine Learning for a Low-cost Air Pollution Network. arXiv preprint arXiv:1911.12868.en_US
dc.identifier.urihttps://arxiv.org/abs/1911.12868
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/7295
dc.language.isoenen_US
dc.publisherarXiv preprinten_US
dc.subjectMachine Learningen_US
dc.subjectLow-costen_US
dc.subjectAir Pollutionen_US
dc.subjectNetworken_US
dc.titleMachine Learning for a Low-cost Air Pollution Networken_US
dc.typeArticleen_US
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