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

Fouzi Harrou; Abdelkader Dairi; Ying Sun; Mohamed Senouci

doi:10.1109/ee1.2018.8385265

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

Fouzi Harrou, Abdelkader Dairi, Ying Sun, Mohamed Senouci

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

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)
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	https://doi.org/10.1109/ee1.2018.8385265
State	Published - Jun 14 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged 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.

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10.1109/ee1.2018.8385265

https://repository.kaust.edu.sa/bitstream/10754/628437/1/08385265.pdf

Cite this

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title = "Reliable detection of abnormal ozone measurements using an air quality sensors network",

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 Is{\`e}re, France. Results demonstrated the capability of the proposed strategy to identify abnormalities in ozone measurements.",

author = "Fouzi Harrou and Abdelkader Dairi and Ying Sun and Mohamed Senouci",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged 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.",

year = "2018",

month = jun,

day = "14",

doi = "10.1109/ee1.2018.8385265",

language = "English (US)",

isbn = "9781538641828",

pages = "1--5",

booktitle = "2018 IEEE International Conference on Environmental Engineering (EE)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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Reliable detection of abnormal ozone measurements using an air quality sensors network. / Harrou, Fouzi; Dairi, Abdelkader; Sun, Ying et al.
2018 IEEE International Conference on Environmental Engineering (EE). Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 1-5.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Reliable detection of abnormal ozone measurements using an air quality sensors network

AU - Harrou, Fouzi

AU - Dairi, Abdelkader

AU - Sun, Ying

AU - Senouci, Mohamed

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged 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.

PY - 2018/6/14

Y1 - 2018/6/14

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/628437

UR - https://ieeexplore.ieee.org/document/8385265/

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U2 - 10.1109/ee1.2018.8385265

DO - 10.1109/ee1.2018.8385265

M3 - Conference contribution

SN - 9781538641828

SP - 1

EP - 5

BT - 2018 IEEE International Conference on Environmental Engineering (EE)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

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

Abstract

Bibliographical note

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