Detecting abnormal ozone measurements with a deep learning-based strategy
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2015-CRG4-2582
Online Publication Date2018-07-02
Print Publication Date2018-09-01
Permanent link to this recordhttp://hdl.handle.net/10754/628364
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AbstractAir quality management and monitoring are vital to maintaining clean air, which is necessary for the health of human, vegetation, and ecosystems. Ozone pollution is one of the main pollutants that negatively affect human health and ecosystems. This paper reports the development of an unsupervised and efficient scheme to detecting anomalies in unlabelled ozone measurements. This scheme combines a Deep Belief Networks (DBN) model and a one-class support vector machine (OCSVM). The DBN model accounts for nonlinear variations in the ground-level ozone concentrations, while OCSVM detects the abnormal ozone measurements. The performance of this approach is evaluated using real data from Is`ere in France. We also compare the detection quality of DBN-based detection schemes to that of deep stacked auto-encoders, Restricted Boltzmann Machinesbased OCSVM and DBN-based clustering procedures (i.e., Kmeans, Birch and Expectation Maximization). The results show that the developed strategy is able to identify anomalies in ozone measurements.
CitationHarrou F, Dairi A, Sun Y, Kadri F (2018) Detecting Abnormal Ozone Measurements With a Deep Learning-Based Strategy. IEEE Sensors Journal 18: 7222–7232. Available: http://dx.doi.org/10.1109/JSEN.2018.2852001.
SponsorsThe research reported in this publication was supported by funding King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.
JournalIEEE Sensors Journal