Reliable detection of abnormal ozone measurements using an air quality sensors network

Type
Conference Paper

Authors
Harrou, Fouzi
Dairi, Abdelkader
Sun, Ying
Senouci, Mohamed

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Statistics Program

KAUST Grant Number
OSR-2015-CRG4-2582

Online Publication Date
2018-06-14

Print Publication Date
2018-03

Date
2018-06-14

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 Isè€re, France. Results demonstrated the capability of the proposed strategy to identify abnormalities in ozone measurements.

Citation
Harrou F, Dairi A, Sun Y, Senouci M (2018) Reliable detection of abnormal ozone measurements using an air quality sensors network. 2018 IEEE International Conference on Environmental Engineering (EE). Available: http://dx.doi.org/10.1109/ee1.2018.8385265.

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.

Publisher
Institute of Electrical and Electronics Engineers (IEEE)

Journal
2018 IEEE International Conference on Environmental Engineering (EE)

DOI
10.1109/ee1.2018.8385265

Additional Links
https://ieeexplore.ieee.org/document/8385265/

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