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. 2018 Mar 22;18(4):938.
doi: 10.3390/s18040938.

Application of Least-Squares Support Vector Machines for Quantitative Evaluation of Known Contaminant in Water Distribution System Using Online Water Quality Parameters

Kexin Wang  1 , Xiang Wen  2 , Dibo Hou  3 , Dezhan Tu  4 , Naifu Zhu  5 , Pingjie Huang  6 , Guangxin Zhang  7 , Hongjian Zhang  8
Affiliations

Application of Least-Squares Support Vector Machines for Quantitative Evaluation of Known Contaminant in Water Distribution System Using Online Water Quality Parameters

Kexin Wang et al. Sensors (Basel). .

Abstract

In water-quality, early warning systems and qualitative detection of contaminants are always challenging. There are a number of parameters that need to be measured which are not entirely linearly related to pollutant concentrations. Besides the complex correlations between variable water parameters that need to be analyzed also impairs the accuracy of quantitative detection. In aspects of these problems, the application of least-squares support vector machines (LS-SVM) is used to evaluate the water contamination and various conventional water quality sensors quantitatively. The various contaminations may cause different correlative responses of sensors, and also the degree of response is related to the concentration of the injected contaminant. Therefore to enhance the reliability and accuracy of water contamination detection a new method is proposed. In this method, a new relative response parameter is introduced to calculate the differences between water quality parameters and their baselines. A variety of regression models has been examined, as result of its high performance, the regression model based on genetic algorithm (GA) is combined with LS-SVM. In this paper, the practical application of the proposed method is considered, controlled experiments are designed, and data is collected from the experimental setup. The measured data is applied to analyze the water contamination concentration. The evaluation of results validated that the LS-SVM model can adapt to the local nonlinear variations between water quality parameters and contamination concentration with the excellent generalization ability and accuracy. The validity of the proposed approach in concentration evaluation for potassium ferricyanide is proven to be more than 0.5 mg/L in water distribution systems.

Keywords: LS-SVM; conventional water-quality sensors; quantitative evaluation; water quality early warning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic of a method for quantitative analysis of contaminant concentration using LS-SVM.
Figure 2
Figure 2
Experimental setup of a small-scale distribution pipe device. PLC: programmable logic controller. Water quality can be monitored at points A, B, C, and D.
Figure 3
Figure 3
Pipeline distribution control system.
Figure 4
Figure 4
Sensor responses for potassium ferricyanide (concentrations: 1.0, 2.0, 4.0, and 8.0 mg L−1).
Figure 5
Figure 5
Relative response values of four parameters with concentration of potassium ferricyanide: (a) TOC; (b) NH3-N; (c) NO3-N; (d) residual chlorine change.
Figure 6
Figure 6
Illustration of comparison between real value and prediction value based on four parameter optimization algorithms.
Figure 7
Figure 7
Sensor responses for potassium ferricyanide (concentrations: 0.5 and 1.0 mg L−1).
Figure 8
Figure 8
Correlation between standard concentration value and evaluated value modeled by LS-SVM.
See this image and copyright information in PMC

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