Smith, M.T., Ssematimba, J., Álvarez, M.A. et al. (1 more author) (Submitted: 2019) Machine Learning for a low-cost air pollution network. [Preprint - arXiv] (Submitted)
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.
Metadata
Item Type: | Preprint |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Author(s). For reuse permissions, please contact the Author(s). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Jan 2025 12:22 |
Last Modified: | 10 Jan 2025 12:22 |
Status: | Submitted |
Identification Number: | 10.48550/arXiv.1911.12868 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:220989 |