Abstract
Ozone pollution is one of the most important pollutants that have a negative effect on human health and the ecosystem. An effective statistical methodology to detect abnormal ozone measurements is proposed in this study. We used a Deep Belief Network model to account for nonlinear variation of ground-level ozone concentrations, in combination with a one-class support vector machine, for detecting abnormal ozone measurement. We assessed the efficiency of this methodology by using real data from a network of air quality monitoring systems in Isère, France. Results demonstrated the capability of the proposed strategy to identify abnormalities in ozone measurements.
Original language | English (US) |
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Title of host publication | 2018 IEEE International Conference on Environmental Engineering (EE) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1-5 |
Number of pages | 5 |
ISBN (Print) | 9781538641828 |
DOIs | |
State | Published - Jun 14 2018 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582
Acknowledgements: The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.