Anomaly Detection in Wastewater Treatment Plants Using Unsupervised Machine Learning Algorithms

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Anomaly Detection in Wastewater Treatment Plants Using Unsupervised Machine Learning Algorithms 23 Pages Posted: 7 Sep 2022 See all articles by Abdulaziz Sami QambarAbdulaziz Sami QambarUniversity of BahrainMohammed Majid Al KhalidyUniversity of Bahrain Abstract The timely detection of unusual wastewater influent contaminants or treatment defaults can significantly mitigate the release of sub-quality treated sewage effluent. Hence, protecting the marine life and the ecosystem from adverse impacts resulting from water pollution. This paper studies and recommends the best anomaly detection algorithm in the wastewater treatment industry. The study was conducted using several unsupervised Machine Learning (ML) algorithms, precisely the Isolation Forest Algorithm (IFA), Fast Minimum Covariance Determinant algorithm (FMCD), Local Outlier Factor algorithm (LOF), and One-Class Support Vector Machines (OCSVM) algorithm. Along with studying the effect of applying Principle Components Analysis on improving anomaly detection performance. In addition, it compared two strategies for applying anomaly detection. The anomaly detection results showed that the FMCD is the most robust algorithm for identifying anomalies in various dimensions, scoring a recall and F1Score using a univariate anomaly detection strategy exceeding 0.90 in four wastewater parameters. Similarly, it scored using a multivariate anomaly detection strategy recall and an F1Score of 0.76. The study found that multivariate anomaly detection and removal strategy is preferred for modeling tasks. However, a univariate anomaly detection strategy is recommended for plant operations to avoid the high dimensionality curse, which deteriorates the model detection performance. The recommended anomaly detection algorithm and strategy can be deployed in an integrated framework of remote sensing and ML to detect wastewater anomalies at wastewater treatment plants, leading to an effective wastewater treatment control that can contribute to preventing environmental pollution. Keywords: anomaly detection, Environment, Machine Learning, remote sensing, Unsupervised Learning, Wastewater Suggested Citation: Suggested Citation Qambar, Abdulaziz Sami and Al Khalidy, Mohammed Majid, Anomaly Detection in Wastewater Treatment Plants Using Unsupervised Machine Learning Algorithms. Available at SSRN: https://ssrn.com/abstract=4191844 Abdulaziz Sami Qambar (Contact Author) University of Bahrain ( email ) P.O Box 32038Sukhair Campus, 32038Bahrain Mohammed Majid Al Khalidy University of Bahrain ( email ) P.O Box 32038Sukhair Campus, 32038Bahrain Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 3 Abstract Views 14 PlumX Metrics Related eJournals Machine Learning eJournal Follow Machine Learning eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 382 PAPERS 5,055 Data Science & Analytics eJournal Follow Data Science & Analytics eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 94 PAPERS 1,814 Informatics eJournal Follow Informatics eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 54 PAPERS 1,034 Libraries & Information Technology eJournal Follow Libraries & Information Technology eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 44 PAPERS 3,423 Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo-5dc in 0.247 seconds