@inproceedings{d70c8a796f9c4c4eaf86be4c8d858483,
title = "Wastewater treatment plant monitoring via a deep learning approach",
abstract = "This paper presents a fault detection method based on an unsupervised deep learning to monitor operating conditions of wastewater treatment plants (WWTPs). This method uses Deep Belief Networks (DBNs) model and one-class support vector machine (OCSVM). Here, DBN model is introduced to account for nonlinear aspects of WWTPs, while OCSVM is employes to reliably detect a fault in WWTP. The developed DBN-OCSVM approach has been tested through practical application on data from a decentralized wastewater treatment plant in Golden, CO, USA. Results show the effectiveness of the developed approach to monitor the WWTP.",
author = "Fouzi Harrou and Abdelkader Dairi and Ying Sun and Mohamed Senouci",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 19th IEEE International Conference on Industrial Technology, ICIT 2018 ; Conference date: 19-02-2018 Through 22-02-2018",
year = "2018",
month = apr,
day = "27",
doi = "10.1109/ICIT.2018.8352410",
language = "English (US)",
series = "Proceedings of the IEEE International Conference on Industrial Technology",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1544--1548",
booktitle = "Proceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018",
address = "United States",
}