NDLI: A Gaussian Process Method with Uncertainty Quantification for Air Quality Monitoring

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A Gaussian Process Method with Uncertainty Quantification for Air Quality Monitoring

Content Provider	MDPI
Author	Wang, Peng Mihaylova, Lyudmila Chakraborty, Rohit Munir, Said Mayfield, Martin Alam, Khan Khokhar, Muhammad Fahim Zheng, Zhengkai Jiang, Chengxi Fang, Hui
Copyright Year	2021
Description	The monitoring and forecasting of particulate matter (e.g., $PM2.5$ ) and gaseous pollutants (e.g., NO, $NO2$ , and $SO2$ ) is of significant importance, as they have adverse impacts on human health. However, model performance can easily degrade due to data noises, environmental and other factors. This paper proposes a general solution to analyse how the noise level of measurements and hyperparameters of a Gaussian process model affect the prediction accuracy and uncertainty, with a comparative case study of atmospheric pollutant concentrations prediction in Sheffield, UK, and Peshawar, Pakistan. The Neumann series is exploited to approximate the matrix inverse involved in the Gaussian process approach. This enables us to derive a theoretical relationship between any independent variable (e.g., measurement noise level, hyperparameters of Gaussian process methods), and the uncertainty and accuracy prediction. In addition, it helps us to discover insights on how these independent variables affect the algorithm evidence lower bound. The theoretical results are verified by applying a Gaussian processes approach and its sparse variants to air quality data forecasting.
Starting Page	1344
e-ISSN	20734433
DOI	10.3390/atmos12101344
Journal	Atmosphere
Issue Number	10
Volume Number	12
Language	English
Publisher	MDPI
Publisher Date	2021-10-14
Access Restriction	Open
Subject Keyword	Atmosphere Gaussian Process Uncertainty Quantification Air Quality Forecasting Low-cost Sensors Sustainable Development
Content Type	Text
Resource Type	Article

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