Adaptive Detection Method for Organic Contamination Events in Water Distribution Systems Using the UV-Vis Spectrum Based on Semi-Supervised Learning

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...

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Vydané v:Water (Basel) Ročník 10; číslo 11; s. 1566
Hlavní autori: Yu, Qiaojun, Yin, Hang, Wang, Ke, Dong, Hui, Hou, Dibo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 02.11.2018
Predmet:
Accuracy
Adaptability
Algorithms
artificial intelligence
Datasets
Drinking water
environmental factors
Methods
monitoring
Organic contaminants
rapid methods
regression analysis
Time series
ultraviolet-visible spectroscopy
water distribution
Water quality
ISSN:2073-4441, 2073-4441
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Shrnutí: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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ISSN:2073-4441
2073-4441
DOI:10.3390/w10111566