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Real-time detection of potable-reclaimed water pipe cross-connection events by conventional water quality sensors using machine learning methods
- Source :
- Journal of environmental management. 238
- Publication Year :
- 2018
-
Abstract
- Risk of cross-connection is becoming higher due to greater construction of potable-reclaimed water dual distribution systems. Cross-connection events can result in serious health concerns and reduce public confidence in reclaimed water. Thus, reliable, cost-effective and real-time online detection methods for early warning are required. The current study carried out pilot-scale experiments to simulate potable-reclaimed water pipe cross-connection events for different mixing ratios (from 30% to 1%) using machine learning methods based on multiple conventional water quality parameters. The parameters included residual chlorine, pH, turbidity, temperature, conductivity, oxidation-reduction potential and chemical oxygen demand. The results showed that correlated variation occurred among water quality parameters at the time of the cross-connection event. A single parameter-based method can be effective at high mixing ratios, but not at low mixing ratios. The direct supporting vector machine (SVM)-based method managed to overcome this drawback, but coped poorly with abnormal readings of water parameter sensors. In that respect, a Pearson correlation coefficient (PCC)-SVM-based method was developed. It provided not only high detection performance under normal conditions, but also remained reliable when abnormal readings occurred. The detection accuracy and true positive rate of this method was still over 88%, and the false positive rate was below 12%, given a sudden variation of an individual water quality parameter. The receiver operating characteristic curves further confirmed the promising practical applicability of this PCC-SVM-based method for early detection of cross-connection events.
- Subjects :
- Environmental Engineering
0208 environmental biotechnology
Water Pipe Smoking
02 engineering and technology
010501 environmental sciences
Management, Monitoring, Policy and Law
Wastewater
Machine learning
computer.software_genre
01 natural sciences
symbols.namesake
Water Supply
Water Quality
Turbidity
Waste Management and Disposal
0105 earth and related environmental sciences
Receiver operating characteristic
business.industry
Drinking Water
Chemical oxygen demand
General Medicine
Pearson product-moment correlation coefficient
Reclaimed water
020801 environmental engineering
Support vector machine
symbols
Environmental science
Artificial intelligence
Water quality
False positive rate
business
computer
Subjects
Details
- ISSN :
- 10958630
- Volume :
- 238
- Database :
- OpenAIRE
- Journal :
- Journal of environmental management
- Accession number :
- edsair.doi.dedup.....476cad321bca9502629e52aa151419c3