Reliable detection of abnormal ozone measurements using an air quality sensors network
Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
KAUST Grant Number
OSR-2015-CRG4-2582Date
2018-06-14Online Publication Date
2018-06-14Print Publication Date
2018-03Permanent link to this record
http://hdl.handle.net/10754/628437
Metadata
Show full item recordAbstract
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.Additional Links
https://ieeexplore.ieee.org/document/8385265/ae974a485f413a2113503eed53cd6c53
10.1109/ee1.2018.8385265