Monitoring Ground-Level Ozone Pollution Based on a Semi-supervised Approach

Benamar Bouyeddou, Fouzi Harrou, Abdelkader Dairi, Ying Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Ozone pollution is among the most harmful pollutants negatively affecting human health. Monitoring abnormal ozone levels concentrations is essential to achieve acceptable air quality. This study introduces a data-driven approach for the early detection of abnormal ozone levels in correlated multivariate data. Importantly, the proposed monitoring scheme combined Restricted Boltzmann Machine (RBM) as a features extractor and a one-class support vector machine (OCSVM) as an anomaly detector. The RBM is considered to learn pertinent information without assumptions on the data distribution, and the OCSVM scheme is employed to discriminate normal ozone data from anomalies. The proposed RBM-OCSVM outperformed the standalone OCSVM scheme when applied to actual multivariate ozone data from France. Results revealed the satisfactory performance of the proposed detector in identifying anomalies in ozone measurements.
Original languageEnglish (US)
Title of host publication2022 7th International Conference on Frontiers of Signal Processing (ICFSP)
ISBN (Print)978-1-6654-8159-5
StatePublished - Oct 28 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-10-31
Acknowledged KAUST grant number(s): OSR2019-CRG7-3800
Acknowledgements: This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR2019-CRG7-3800.


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