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.
Highlights
IntroductionThe idea of establishing an early warning system (EWS) to make the water supply system more robust against contamination events has been highlighted
With the progress of urbanization, there are increasing management problems concerning the pollution of water bodies or drinking water, which troubles many countries, especially developing ones.The idea of establishing an early warning system (EWS) to make the water supply system more robust against contamination events has been highlighted
This paper introduces the water-contamination quantitative evaluation method using multiple conventional water quality parameters, which comes to the following conclusions: Firstly, based on the phenomenon that different contaminations may cause different correlative responses of sensors and the degree of responses is related to the injected concentration of contamination, the experiment results imply that this type of phenomenon can be used for quantitative analysis of a known contamination incident in a water distribution system
Summary
The idea of establishing an early warning system (EWS) to make the water supply system more robust against contamination events has been highlighted. Numerous studies has been performed on water quality early warning technologies all over the world, including water quality sensor technologies, event detection algorithms, hydrological models, and decision-making systems (DSS) [2,3]. Establishing an early warning system has been recognized as an effective means of: (1) avoiding or reducing the impact of water contamination events; and (2) protecting water sources and ensuring the safety of drinking water [4]. The online conventional water quality sensor techniques of water events detection are mainly divided into three categories, artificial intelligence (AI), statistical approach, and data mining method [5] respectively. As for data mining, it is used to protect drinking water systems by combining various sensors measurement values and location information [6,8]
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