{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T20:40:08Z","timestamp":1790368808840,"version":"4.1.0"},"reference-count":25,"publisher":"IWA Publishing","issue":"12","license":[{"start":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T00:00:00Z","timestamp":1686096000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["iwaponline.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p\/>\n               <jats:p>To prevent the pollution of water resources, the measurement and the limitation of wastewater discharges are required. Despite the progress in the field of data acquisition systems, sensors are subject to malfunctions that can bias the evaluation of the pollution flow. It is therefore essential to identify potential anomalies in the data before any use. The objective of this work is to deploy artificial intelligence tools to automate the data validation and to assess the added value of this approach in assisting the validation performed by an operator. To do so, we compare two state-of-the-art anomaly detection algorithms on turbidity data in a sewer network. On the one hand, we conclude that the One-class SVM model is not adapted to the nature of the studied data which is heterogeneous and noisy. The Matrix Profile model, on the other hand, provides promising results with a majority of anomalies detected and a relatively limited number of false positives. By comparing these results to the expert validation, it turns out that the use of the Matrix Profile model objectifies and accelerates the validation task while maintaining the same level of performance compared to the annotator agreement rate between two experts.<\/jats:p>","DOI":"10.2166\/wst.2023.174","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T18:08:17Z","timestamp":1686161297000},"page":"2957-2970","update-policy":"https:\/\/doi.org\/10.2166\/iwapcrossmarkpolicypage","source":"Crossref","is-referenced-by-count":14,"title":["Validation of wastewater data using artificial intelligence tools and the evaluation of their performance regarding annotator agreement"],"prefix":"10.2166","volume":"87","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9667-7061","authenticated-orcid":false,"given":"Imane","family":"Zidaoui","sequence":"first","affiliation":[{"name":"a Department of Fluid Mechanics, ICube Laboratory, 2 Rue Boussingault, Strasbourg 67000, France"},{"name":"b 3D EAU, 3 Quai Kl\u00e9ber, Strasbourg 67000, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4360-4918","authenticated-orcid":false,"given":"C\u00e9dric","family":"Wemmert","sequence":"additional","affiliation":[{"name":"c Data Science and Knowledge Department, ICube Laboratory, 300 Bd S\u00e9bastien Brant, Illkirch-Graffenstaden 67400, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthieu","family":"Dufresne","sequence":"additional","affiliation":[{"name":"b 3D EAU, 3 Quai Kl\u00e9ber, Strasbourg 67000, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claude","family":"Joannis","sequence":"additional","affiliation":[{"name":"d CJ Conseil, 37 Rue du Coteau, Nantes 44100, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandra","family":"Isel","sequence":"additional","affiliation":[{"name":"b 3D EAU, 3 Quai Kl\u00e9ber, Strasbourg 67000, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Wertel","sequence":"additional","affiliation":[{"name":"b 3D EAU, 3 Quai Kl\u00e9ber, Strasbourg 67000, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9065-8559","authenticated-orcid":false,"given":"Jos\u00e9","family":"Vazquez","sequence":"additional","affiliation":[{"name":"a Department of Fluid Mechanics, ICube Laboratory, 2 Rue Boussingault, Strasbourg 67000, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"945","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"key":"key-10.2166\/wst.2023.174-1","doi-asserted-by":"publisher","DOI":"10.2175\/193864713813686060"},{"key":"key-10.2166\/wst.2023.174-2","doi-asserted-by":"publisher","DOI":"10.2166\/wst.2010.350"},{"key":"key-10.2166\/wst.2023.174-3","doi-asserted-by":"publisher","DOI":"10.1080\/1573062X.2013.763996"},{"key":"key-10.2166\/wst.2023.174-4","doi-asserted-by":"publisher","DOI":"10.1080\/1573062X.2019.1637002"},{"key":"key-10.2166\/wst.2023.174-5","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/978-3-030-04110-6_7","article-title":"Blockchain and Internet of Things-based technologies for intelligent water management system","volume-title":"Artificial Intelligence in IoT","author":"Dogo","year":"2019b"},{"key":"key-10.2166\/wst.2023.174-6","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1007\/978-3-030-46150-8_15","article-title":"Pattern-based anomaly detection in mixed-type time series","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"Feremans","year":"2020"},{"key":"key-10.2166\/wst.2023.174-7","first-page":"1058","article-title":"Anomaly detection for a water treatment system using unsupervised machine learning","author":"Inoue","year":"2017"},{"key":"key-10.2166\/wst.2023.174-8","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2544703"},{"key":"key-10.2166\/wst.2023.174-9","unstructured":"Methnani S. 2012 Diagnostic, reconstruction et identification des d\u00e9fauts capteurs et actionneurs: application aux station d\u2019\u00e9purations des eaux us\u00e9es (Diagnosis, Reconstruction and Identification of Sensor and Actuator Faults: Application to Wastewater Treatment Plants). PhD Thesis, University of Toulon (France), National School of Engineers of Sfax (Tunisie)."},{"key":"key-10.2166\/wst.2023.174-10","doi-asserted-by":"publisher","DOI":"10.2166\/wst.2002.0601"},{"key":"key-10.2166\/wst.2023.174-11","doi-asserted-by":"publisher","DOI":"10.1080\/24751839.2019.1565653"},{"key":"key-10.2166\/wst.2023.174-12","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-1694(00)00213-4"},{"key":"key-10.2166\/wst.2023.174-13","volume-title":"Automatic Outlier Detection in Automated Water Quality Measurement Stations. 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J., 2020 Current Time Series Anomaly Detection Benchmarks Are Flawed and Are Creating the Illusion of Progress. https:\/\/doi.org\/10.48550\/ARXIV.2009.13807","DOI":"10.1109\/TKDE.2021.3112126"},{"key":"key-10.2166\/wst.2023.174-22","first-page":"1317","article-title":"Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets","author":"Yeh","year":"2016"},{"key":"key-10.2166\/wst.2023.174-23","unstructured":"Zhang R., Zhang S., Lan Y. & Jiang J. 2008 Network Anomaly Detection Using One Class Support Vector Machine. Computer Science, Hong Kong, China."},{"key":"key-10.2166\/wst.2023.174-24","first-page":"36","article-title":"A real-time anomaly detection algorithm\/or water quality data using dual time-moving windows","author":"Zhang","year":"2017"},{"key":"key-10.2166\/wst.2023.174-25","doi-asserted-by":"publisher","DOI":"10.36904\/tsm\/202211039"}],"container-title":["Water Science &amp; Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/iwaponline.com\/wst\/article-pdf\/87\/12\/2957\/1245818\/wst087122957.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iwaponline.com\/wst\/article-pdf\/87\/12\/2957\/1245818\/wst087122957.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T16:42:49Z","timestamp":1688143369000},"score":1,"resource":{"primary":{"URL":"https:\/\/iwaponline.com\/wst\/article\/87\/12\/2957\/95515\/Validation-of-wastewater-data-using-artificial"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,7]]},"references-count":25,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6,7]]},"published-print":{"date-parts":[[2023,6,15]]}},"URL":"https:\/\/doi.org\/10.2166\/wst.2023.174","relation":{},"ISSN":["0273-1223","1996-9732"],"issn-type":[{"value":"0273-1223","type":"print"},{"value":"1996-9732","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,7]]}}}