Browsing by Author "Smith, Michael T."

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    Gaussian Process Models for Low Cost Air Quality Monitoring
    (University of Makerere, 2021) Smith, Michael T.; Ssematimba, Joel; Alvarez, Mauricio A.; Bainomugisha, Engineer
    Air pollution contributes to over three million deaths [1] each year. Kampala has one of the highest concentrations of fine particulate matter (PM 2.5) of any African city [2]. Unfortunately, with the exception of the US Embassy, there is no programme for monitoring air pollution in the city due to the high cost of the equipment required. Hence we know little about its distribution or extent. Lower cost devices do exist, but these do not, on their own, provide the accuracy required for decision makers. We propose that using a coregionalised Gaussian process to combine the low cost sensors with the embassy’s high quality results provides sufficiently accurate estimates of pollution across the city.
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    Machine Learning for a Low-cost Air Pollution Network
    (arXiv preprint, 2019) Smith, Michael T.; Ssematimba, Joel; Álvarez, Mauricio A.; Bainomugisha, Engineer
    Data 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.