Reliable detection of abnormal ozone measurements using an air quality sensors network
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2015-CRG4-2582
Online Publication Date2018-06-14
Print Publication Date2018-03
Permanent link to this recordhttp://hdl.handle.net/10754/628437
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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.
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.
SponsorsThe 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.