Machine Learning for a Low-cost Air Pollution Network
Loading...
Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
arXiv preprint
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.
Description
Keywords
Machine Learning, Low-cost, Air Pollution, Network
Citation
Smith, M. T., Ssematimba, J., Alvarez, M. A., & Bainomugisha, E. (2019). Machine Learning for a Low-cost Air Pollution Network. arXiv preprint arXiv:1911.12868.