Risk assessment for early water leak detection

Wilmer P. Cantos, Ilan Juran, Silvia Tinelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents the automation of the current leak detection operators experience and practice. A historical data driven statistical based approach is validated for real time leak detection in water distribution systems (WDS) yielding a color coded leak detection indicators (Likelihood LI, Severity SI, and Risk RI), which are integrated and visualized in an operator's user-friendly interface. This paper presents four signature scenarios using 3-year synthetic data from Eau de Paris monitored data. The data simulates leak scenarios in the WDS of the experimental demonstration site of Lille University Campus, France. The research includes: (1) assessment of the current state of practice; (2) development of an intelligent network control and on-site monitoring (INCOM) prototype system for early network leak detection; and (3) demonstration and assessment of the INCOM prototype system through off-line scenarios simulations. It supports the system operator(s) in "intelligent" real-time system monitoring and leak detection for preventive and proactive rather than reactive water distribution system management.

Original languageEnglish (US)
Title of host publicationInternational Conference on Sustainable Infrastructure 2017
Subtitle of host publicationTechnology - Proceedings of the International Conference on Sustainable Infrastructure 2017
PublisherAmerican Society of Civil Engineers (ASCE)
Pages306-318
Number of pages13
ISBN (Electronic)9780784481219
DOIs
StatePublished - Jan 1 2017
Event2017 International Conference on Sustainable Infrastructure: Technology, ICSI 2017 - New York, United States
Duration: Oct 26 2017Oct 28 2017

Other

Other2017 International Conference on Sustainable Infrastructure: Technology, ICSI 2017
CountryUnited States
CityNew York
Period10/26/1710/28/17

Fingerprint

Leak detection
Risk assessment
Water distribution systems
Intelligent networks
Water
Monitoring
Demonstrations
Real time systems
User interfaces
Automation
Color
Scenarios
Operator
Distribution system

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Renewable Energy, Sustainability and the Environment
  • Management of Technology and Innovation
  • Safety, Risk, Reliability and Quality

Cite this

Cantos, W. P., Juran, I., & Tinelli, S. (2017). Risk assessment for early water leak detection. In International Conference on Sustainable Infrastructure 2017: Technology - Proceedings of the International Conference on Sustainable Infrastructure 2017 (pp. 306-318). American Society of Civil Engineers (ASCE). https://doi.org/10.1061/9780784481219.027

Risk assessment for early water leak detection. / Cantos, Wilmer P.; Juran, Ilan; Tinelli, Silvia.

International Conference on Sustainable Infrastructure 2017: Technology - Proceedings of the International Conference on Sustainable Infrastructure 2017. American Society of Civil Engineers (ASCE), 2017. p. 306-318.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Cantos, WP, Juran, I & Tinelli, S 2017, Risk assessment for early water leak detection. in International Conference on Sustainable Infrastructure 2017: Technology - Proceedings of the International Conference on Sustainable Infrastructure 2017. American Society of Civil Engineers (ASCE), pp. 306-318, 2017 International Conference on Sustainable Infrastructure: Technology, ICSI 2017, New York, United States, 10/26/17. https://doi.org/10.1061/9780784481219.027
Cantos WP, Juran I, Tinelli S. Risk assessment for early water leak detection. In International Conference on Sustainable Infrastructure 2017: Technology - Proceedings of the International Conference on Sustainable Infrastructure 2017. American Society of Civil Engineers (ASCE). 2017. p. 306-318 https://doi.org/10.1061/9780784481219.027
Cantos, Wilmer P. ; Juran, Ilan ; Tinelli, Silvia. / Risk assessment for early water leak detection. International Conference on Sustainable Infrastructure 2017: Technology - Proceedings of the International Conference on Sustainable Infrastructure 2017. American Society of Civil Engineers (ASCE), 2017. pp. 306-318
@inproceedings{2d8c6a3203a5438c83ee996d2d68a817,
title = "Risk assessment for early water leak detection",
abstract = "This paper presents the automation of the current leak detection operators experience and practice. A historical data driven statistical based approach is validated for real time leak detection in water distribution systems (WDS) yielding a color coded leak detection indicators (Likelihood LI, Severity SI, and Risk RI), which are integrated and visualized in an operator's user-friendly interface. This paper presents four signature scenarios using 3-year synthetic data from Eau de Paris monitored data. The data simulates leak scenarios in the WDS of the experimental demonstration site of Lille University Campus, France. The research includes: (1) assessment of the current state of practice; (2) development of an intelligent network control and on-site monitoring (INCOM) prototype system for early network leak detection; and (3) demonstration and assessment of the INCOM prototype system through off-line scenarios simulations. It supports the system operator(s) in {"}intelligent{"} real-time system monitoring and leak detection for preventive and proactive rather than reactive water distribution system management.",
author = "Cantos, {Wilmer P.} and Ilan Juran and Silvia Tinelli",
year = "2017",
month = "1",
day = "1",
doi = "10.1061/9780784481219.027",
language = "English (US)",
pages = "306--318",
booktitle = "International Conference on Sustainable Infrastructure 2017",
publisher = "American Society of Civil Engineers (ASCE)",
address = "United States",

}

TY - GEN

T1 - Risk assessment for early water leak detection

AU - Cantos, Wilmer P.

AU - Juran, Ilan

AU - Tinelli, Silvia

PY - 2017/1/1

Y1 - 2017/1/1

N2 - This paper presents the automation of the current leak detection operators experience and practice. A historical data driven statistical based approach is validated for real time leak detection in water distribution systems (WDS) yielding a color coded leak detection indicators (Likelihood LI, Severity SI, and Risk RI), which are integrated and visualized in an operator's user-friendly interface. This paper presents four signature scenarios using 3-year synthetic data from Eau de Paris monitored data. The data simulates leak scenarios in the WDS of the experimental demonstration site of Lille University Campus, France. The research includes: (1) assessment of the current state of practice; (2) development of an intelligent network control and on-site monitoring (INCOM) prototype system for early network leak detection; and (3) demonstration and assessment of the INCOM prototype system through off-line scenarios simulations. It supports the system operator(s) in "intelligent" real-time system monitoring and leak detection for preventive and proactive rather than reactive water distribution system management.

AB - This paper presents the automation of the current leak detection operators experience and practice. A historical data driven statistical based approach is validated for real time leak detection in water distribution systems (WDS) yielding a color coded leak detection indicators (Likelihood LI, Severity SI, and Risk RI), which are integrated and visualized in an operator's user-friendly interface. This paper presents four signature scenarios using 3-year synthetic data from Eau de Paris monitored data. The data simulates leak scenarios in the WDS of the experimental demonstration site of Lille University Campus, France. The research includes: (1) assessment of the current state of practice; (2) development of an intelligent network control and on-site monitoring (INCOM) prototype system for early network leak detection; and (3) demonstration and assessment of the INCOM prototype system through off-line scenarios simulations. It supports the system operator(s) in "intelligent" real-time system monitoring and leak detection for preventive and proactive rather than reactive water distribution system management.

UR - http://www.scopus.com/inward/record.url?scp=85035136082&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85035136082&partnerID=8YFLogxK

U2 - 10.1061/9780784481219.027

DO - 10.1061/9780784481219.027

M3 - Conference contribution

AN - SCOPUS:85035136082

SP - 306

EP - 318

BT - International Conference on Sustainable Infrastructure 2017

PB - American Society of Civil Engineers (ASCE)

ER -