{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T04:01:17Z","timestamp":1778904077953,"version":"3.51.4"},"reference-count":24,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,3,13]],"date-time":"2017-03-13T00:00:00Z","timestamp":1489363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Water quality early warning system is mainly used to detect deliberate or accidental water pollution events in water distribution systems. Identifying the types of pollutants is necessary after detecting the presence of pollutants to provide warning information about pollutant characteristics and emergency solutions. Thus, a real-time contaminant classification methodology, which uses the multi-classification support vector machine (SVM), is proposed in this study to obtain the probability for contaminants belonging to a category. The SVM-based model selected samples with indistinct feature, which were mostly low-concentration samples as the support vectors, thereby reducing the influence of the concentration of contaminants in the building process of a pattern library. The new sample points were classified into corresponding regions after constructing the classification boundaries with the support vector. Experimental results show that the multi-classification SVM-based approach is less affected by the concentration of contaminants when establishing a pattern library compared with the cosine distance classification method. Moreover, the proposed approach avoids making a single decision when classification features are unclear in the initial phase of injecting contaminants.<\/jats:p>","DOI":"10.3390\/s17030581","type":"journal-article","created":{"date-parts":[[2017,3,13]],"date-time":"2017-03-13T10:26:18Z","timestamp":1489400778000},"page":"581","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Online Classification of Contaminants Based on Multi-Classification Support Vector Machine Using Conventional Water Quality Sensors"],"prefix":"10.3390","volume":"17","author":[{"given":"Pingjie","family":"Huang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4250-3637","authenticated-orcid":false,"given":"Yu","family":"Jin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dibo","family":"Hou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Yu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dezhan","family":"Tu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yitong","family":"Cao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,13]]},"reference":[{"key":"ref_1","unstructured":"Toolbox, R.P. (2003). Planning for and Responding to Drinking Water Contamination Threats and Incidents."},{"key":"ref_2","unstructured":"USEPA (2005). Water sentinel System Architecture Draft Version 1.0."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2678","DOI":"10.1021\/es052035a","article-title":"HPLC-DAD and Q-TOF MS techniques identify cause of Daphnia biomonitor alarms in the River Meuse","volume":"8","author":"Hoogh","year":"2006","journal-title":"Environ. Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"509","DOI":"10.2166\/aqua.2005.0045","article-title":"A review of analytical methods for assessing the public health risk from microcystin in the aquatic environment","volume":"8","author":"Hawkins","year":"2005","journal-title":"J. Water Supply Res. Technol.-Aqua"},{"key":"ref_5","unstructured":"Kroll, D.J. (2006). Securing Our Water Supply: Protecting a Vulnerable Resource, PennWell Books."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.1016\/j.jenvman.2009.01.021","article-title":"Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: Techniques and experimental results","volume":"8","author":"Jeffrey","year":"2009","journal-title":"J. Environ. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.jenvman.2015.02.023","article-title":"A real time method of contaminant classification using conventional water quality sensors","volume":"154","author":"Liu","year":"2015","journal-title":"J. Environ. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1039\/C4EM00580E","article-title":"Contaminant classification using cosine distances based on multiple conventional sensors","volume":"2","author":"Liu","year":"2015","journal-title":"Environ. Sci. Process. Impacts"},{"key":"ref_9","first-page":"88","article-title":"A transformer fault diagnosing method based on multi-classified probability output","volume":"39","author":"Bi","year":"2015","journal-title":"Autom. Electr. Power Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1006\/mssp.2001.1454","article-title":"Fault Detection Using Support Vector Machines and Artificial Neural Networks, Augmented by Genetic Algorithms","volume":"2","author":"Jack","year":"2002","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_11","first-page":"1622","article-title":"Support Vector Machines Based Approach for Fault Diagnosis of Valves in Reciprocating Pumps","volume":"3","author":"Shi","year":"2002","journal-title":"J. Mech. Strength"},{"key":"ref_12","first-page":"847","article-title":"Application of Support Vector Machines in Financial Time Series Forecasting","volume":"1","author":"Meng","year":"2002","journal-title":"Neurocomputing"},{"key":"ref_13","first-page":"034","article-title":"Support Vector Machine Based Nonlinear Systems Identification","volume":"1","author":"Zhang","year":"2003","journal-title":"Acta Simulata Syst. Sinica"},{"key":"ref_14","unstructured":"Yan, W., and Shao, H. (2002, January 10\u201314). Application of support vector machine nonlinear classifier to fault diagnoses. Proceedings of the World Congress on Intelligent Control and Automation, Shanghai, China."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.watres.2013.10.060","article-title":"A coupled classification\u2013Evolutionary optimization model for contamination event detection in water distribution systems","volume":"51","author":"Oliker","year":"2014","journal-title":"Water Res."},{"key":"ref_16","first-page":"735","article-title":"Multi-parameters fusion algorithm for detecting anomalous water quality","volume":"4","author":"He","year":"2013","journal-title":"J. Zhejiang Univ."},{"key":"ref_17","first-page":"138","article-title":"Water quality anomaly detection method based on RBF neural network and wavelet analysis","volume":"2","author":"Hou","year":"2013","journal-title":"Transducer Microsyst. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"McKenna, S.A., Hart, D.B., Klise, K.A., Cruz, V.A., and Wilson, M.P. (2007, January 15\u201319). Event detection from water quality time series. Proceedings of the World Environmental and Water Resources Congress 2007: Restoring Our Natural Habitat, Tampa, FL, USA.","DOI":"10.1061\/40927(243)518"},{"key":"ref_19","unstructured":"Vapnik, V. (2013). The Nature of Statistical Learning Theory, Springer."},{"key":"ref_20","first-page":"3185","article-title":"Statistical Learning Theory","volume":"4","author":"Vapnik","year":"2010","journal-title":"Encycl. Sci. Learn."},{"key":"ref_21","first-page":"389","article-title":"LIBSVM: A library for support vector machines","volume":"3","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"61","DOI":"10.7551\/mitpress\/1113.003.0008","article-title":"Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods","volume":"10","author":"Platt","year":"2000","journal-title":"Adv. Large Margin Classif."},{"key":"ref_23","first-page":"975","article-title":"Probability estimates for multi-class classification by pairwise coupling","volume":"5","author":"Wu","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_24","first-page":"451","article-title":"Classification by pairwise coupling","volume":"2","author":"Hastie","year":"1998","journal-title":"Ann. Stat."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/3\/581\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:30:21Z","timestamp":1760207421000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/3\/581"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,13]]},"references-count":24,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2017,3]]}},"alternative-id":["s17030581"],"URL":"https:\/\/doi.org\/10.3390\/s17030581","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,3,13]]}}}