+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Real-time detection of potable-reclaimed water pipe cross-connection events by conventional water quality sensors using machine learning methods



Real-time detection of potable-reclaimed water pipe cross-connection events by conventional water quality sensors using machine learning methods



Journal of Environmental Management 238: 201-209



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.

Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 066572143

Download citation: RISBibTeXText

PMID: 30851559

DOI: 10.1016/j.jenvman.2019.02.110


Related references

Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: techniques and experimental results. Journal of Environmental Management 90(8): 2494-2506, 2009

A real time method of contaminant classification using conventional water quality sensors. Journal of Environmental Management 154: 13-21, 2016

Contamination event detection using multiple types of conventional water quality sensors in source water. Environmental Science. Processes and Impacts 16(8): 2028-2038, 2015

Achieving potable water quality through advanced reclaimed water treatment processes of a membrane pilot plant. Abstracts of the General Meeting of the American Society for 106): 471, 2006

Tertiary treatment and dual disinfection to improve microbial quality of reclaimed water for potable and non-potable reuse: A case study of facilities in North Carolina. Science of the Total Environment 630: 379-388, 2018

Tertiary treatment and dual disinfection to improve microbial quality of reclaimed water for potable and non-potable reuse: A case study of facilities in North Carolina. Science of Total Environment 630: 379-388, 2018

Near Real-Time Detection of E. coli in Reclaimed Water. Sensors 18(7), 2018

Comparison of conventional culture methods and quantitative real-time PCR methods for the detection of Legionella pneumophila in water samples in a large University teaching hospital in Rome, Italy. Igiene E Sanita Pubblica 71(6): 569-576, 2016

A method of detecting contamination events using multiple conventional water quality sensors. Environmental Monitoring and Assessment 187(1): 4189, 2015

Machine learning in real-time control of water systems. Urban Water 4(3): 283-289, 2002

Optical sensors for real time detection of bacterial contamination in water systems. Abstracts of Papers American Chemical Society 226(1-2): ANYL 103, 2003

Feasibility of using a device called ultraviolet for operational control of the quality of reclaimed potable water. Kosmicheskaya Biologiya i Aviakosmicheskaya Meditsina 14(5): 77-79, 1980

Online Classification of Contaminants Based on Multi-Classification Support Vector Machine Using Conventional Water Quality Sensors. Sensors 17(3), 2018

Real Time Foot Drop Correction using Machine Learning and Natural Sensors. Neuromodulation 5(1): 41-53, 2002

Banking stormwater, reclaimed water and potable water in aquifers. Pages 71-80 2002, 2002