• Login
    View Item 
    •   NRU
    • Journal Publications
    • Engineering and Technology
    • Engineering and Technology
    • View Item
    •   NRU
    • Journal Publications
    • Engineering and Technology
    • Engineering and Technology
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Machine Learning for a Low-cost Air Pollution Network

    Thumbnail
    View/Open
    Article (1.568Mb)
    Date
    2019
    Author
    Smith, Michael T.
    Ssematimba, Joel
    Álvarez, Mauricio A.
    Bainomugisha, Engineer
    Metadata
    Show full item record
    Abstract
    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.
    URI
    https://arxiv.org/abs/1911.12868
    https://nru.uncst.go.ug/handle/123456789/7295
    Collections
    • Engineering and Technology [839]

    Research Dissemination Platform copyright © since 2021  UNCST
    Contact Us | Send Feedback
    Partners
     

     

    Browse

    All of NRU
    Communities & CollectionsBy Issue DateAuthorsTitlesSubjects
    This Collection
    By Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    Research Dissemination Platform copyright © since 2021  UNCST
    Contact Us | Send Feedback
    Partners