期刊
WATER
卷 10, 期 11, 页码 -出版社
MDPI
DOI: 10.3390/w10111566
关键词
UV-Vis spectrum; organic contamination events detection; dynamic orthogonal projection correction; support vector regression; semi-supervised learning
资金
- National Natural Science Foundation of China [61573313, U1509208]
- National Key R&D Program of China [2017YFC1403801]
- Key Technology Research and Development Program of Zhejiang Province [2015C03G2010034]
- Fundamental Research Funds for the Central Universities [2017FZA5011]
A method that uses the ultraviolet-visible (UV-Vis) spectrum to detect organic contamination events in water distribution systems exhibits the advantages of rapid detection, low cost, and no need for reagents. The speed, accuracy, and comprehensive analysis of such a method meet the requirements for online water quality monitoring. However, the UV-Vis spectrum is easily disturbed by environmental factors that cause fluctuations of the spectrum and result in false alarms. This study proposes an adaptive method for detecting organic contamination events in water distribution systems that uses the UV-Vis spectrum based on a semi-supervised learning model. This method modifies the baseline using dynamic orthogonal projection correction and adjusts the support vector regression model in real time. Thus, an adaptive online anomaly detection model that maximizes the use of unlabeled data is obtained. Experimental results demonstrate that the proposed method is adaptive to baseline drift and exhibits good performance in detecting organic contamination events in water distribution systems.
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