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Sensors 2017, 17(3), 581; doi:10.3390/s17030581

Online Classification of Contaminants Based on Multi-Classification Support Vector Machine Using Conventional Water Quality Sensors

State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
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Academic Editor: Debbie G. Senesky
Received: 9 December 2016 / Revised: 14 February 2017 / Accepted: 9 March 2017 / Published: 13 March 2017
(This article belongs to the Special Issue Sensors for Environmental Monitoring 2016)
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Abstract

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. View Full-Text
Keywords: early warning systems; contaminant classification; conventional water quality sensors; support vector machine; multi-classification probability output early warning systems; contaminant classification; conventional water quality sensors; support vector machine; multi-classification probability output
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Huang, P.; Jin, Y.; Hou, D.; Yu, J.; Tu, D.; Cao, Y.; Zhang, G. Online Classification of Contaminants Based on Multi-Classification Support Vector Machine Using Conventional Water Quality Sensors. Sensors 2017, 17, 581.

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