Detecting abnormal ozone measurements with a deep learning-based strategy

Fouzi Harrou, Abdelkader Dairi, Ying Sun, Farid Kadri

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


Air 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.
Original languageEnglish (US)
Pages (from-to)7222-7232
Number of pages11
JournalIEEE Sensors Journal
Issue number17
StatePublished - Jul 2 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582
Acknowledgements: The 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.


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