Wang Kexin, Wen Xiang, Hou Dibo, Tu Dezhan, Zhu Naifu, Huang Pingjie, Zhang Guangxin, Zhang Hongjian
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel). 2018 Mar 22;18(4):938. doi: 10.3390/s18040938.
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.
在水质方面，早期预警系统和污染物的定性检测一直具有挑战性。需要测量许多参数，这些参数与污染物浓度并非完全呈线性相关。此外，需要分析的各种可变水参数之间的复杂相关性也会影响定量检测的准确性。针对这些问题，采用最小二乘支持向量机（LS-SVM）来定量评估水污染情况以及各种传统水质传感器。各种污染物可能会导致传感器产生不同的相关响应，而且响应程度与注入污染物的浓度有关。因此，为提高水污染检测的可靠性和准确性，提出了一种新方法。在该方法中，引入了一个新的相对响应参数来计算水质参数与其基线之间的差异。已经考察了多种回归模型，由于基于遗传算法（GA）的回归模型性能优异，将其与LS-SVM相结合。本文考虑了所提方法的实际应用，设计了对照实验，并从实验装置中收集数据。将测量数据用于分析水污染浓度。结果评估验证了LS-SVM模型能够适应水质参数与污染浓度之间的局部非线性变化，具有出色的泛化能力和准确性。所提方法在配水系统中对铁氰化钾浓度评估的有效性被证明在0.5mg/L以上。
