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dc.contributor.authorHarrou, Fouzi
dc.contributor.authorDairi, Abdelkader
dc.contributor.authorSun, Ying
dc.contributor.authorSenouci, Mohamed
dc.date.accessioned2018-09-03T13:23:36Z
dc.date.available2018-09-03T13:23:36Z
dc.date.issued2018-06-14
dc.identifier.citationHarrou 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.
dc.identifier.doi10.1109/ee1.2018.8385265
dc.identifier.urihttp://hdl.handle.net/10754/628437
dc.description.abstractOzone 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.
dc.description.sponsorshipThe 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.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8385265/
dc.rightsArchived with thanks to 2018 IEEE International Conference on Environmental Engineering (EE)
dc.subjectOzone pollution
dc.subjectmachine learning
dc.subjectstatistical monitoring
dc.subjectanomaly detection
dc.titleReliable detection of abnormal ozone measurements using an air quality sensors network
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journal2018 IEEE International Conference on Environmental Engineering (EE)
dc.eprint.versionPost-print
dc.contributor.institutionComputer Science Department, University of Oran 1 Ahmed Ben Bella , Street El senia el mnouer bp 31000 Oran, Algeria
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.grant.numberOSR-2015-CRG4-2582
refterms.dateFOA2018-09-06T12:53:09Z
dc.date.published-online2018-06-14
dc.date.published-print2018-03


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