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

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    08385265.pdf
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    Type
    Conference Paper
    Authors
    Harrou, Fouzi cc
    Dairi, Abdelkader
    Sun, Ying cc
    Senouci, Mohamed
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    KAUST Grant Number
    OSR-2015-CRG4-2582
    Date
    2018-06-14
    Online Publication Date
    2018-06-14
    Print Publication Date
    2018-03
    Permanent link to this record
    http://hdl.handle.net/10754/628437
    
    Metadata
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    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.
    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.
    Sponsors
    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/
    ae974a485f413a2113503eed53cd6c53
    10.1109/ee1.2018.8385265
    Scopus Count
    Collections
    Conference Papers; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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