Machine-Learning–Based Risk Assessment Method for Leak Detection and Geolocation in a Water Distribution System
Publication: Journal of Infrastructure Systems
Volume 26, Issue 1
Abstract
Current leak detection practice in a water distribution system consists of monitoring the distributed volume in a district metering area (DMA) and the consumption measured with automated meter reading (AMR) at the building connections. The detection of the occurrence of a potential leak in a DMA is established through a systematic continuous comparison of the real-time distributed volume and the consumption for this DMA and/or, in the absence of AMR, the comparison of the monitored distributed volume and a reference curve based upon past monitoring records of the distributed volume under similar operational conditions. The purpose of this research was to develop, test, validate, and illustrate the application of the machine-learning–based risk assessment method for early detection of high likelihood leaks, their geolocation, and the detection accuracy assessment in the water distribution system of the SUNRISE demonstration site at the University of Lille, France. It illustrates that the proposed algorithm, integrated with a GIS-based spatial flow data analysis, efficiently supports early detection, likelihood severity assessment, and geolocation of leak sources.
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Data Availability Statement
All data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
The authors acknowledge Prof. Isam Shahrour and the team at the Laboratory of Civil Engineering and Geo–Environment (LGCgE) at Lille 1 University, France, as well as the team at NYU Data Services, especially Denis Rubin, Senior Academic Technology Specialist, Quantitative Lead.
References
ASCE. 2017. “Report card for America’s infrastructure.” https://www.infrastructurereportcard.org/.
Cantos, W. P. 2018. Artificial intelligence application for leak detection and geolocation in water distribution systems.” Doctoral dissertation, New York Univ. Tandon School of Engineering.
Cantos, W. P., and I. Juran. 2019. “Infrastructure aging risk assessment for water distribution systems” Water Sci. Technol. Water Supply 19 (3): 899–907. https://doi.org/10.2166/ws.2018.139.
Cantos, W. P., I. Juran, and S. Tinelli. 2017. “Risk assessment for early water leak detection.” In Proc., Int. Conf. on Sustainable Infrastructure: Technology, ICSI. Reston, VA: ASCE.
DeSilva, D., S. Burn, M. Eiswirth, O. Hunaidi, A. Speers, and J. Thornton. 1999. Pipe leak–Future challenges and solutions. Wagga Wagga, Australia.
Farah, E. (2016). “Detection of water leak using innovative smart water system: Application to sunrise smart city demonstrator.” Doctoral dissertation, Lille Univ.
Farah, E., and I. Shahrour. 2017. “Leakage detection using smart water system: Combination of water balance and automated minimum night flow” Water Resour. Manage. 31 (15): 4821–4833. https://doi.org/10.1007/s11269-017-1780-9.
Jafar, R., I. Shahrour, and I. Juran. 2010. “Application of Artificial Neural Networks (ANN) to model the failure of urban water mains” Math. Comput. Modell. 51 (9): 1170–1180. https://doi.org/10.1016/j.mcm.2009.12.033.
LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep learning” Nat. 521 (7553): 436. https://doi.org/10.1038/nature14539.
Maier, H. R., and G. C. Dandy. 2002. "Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications." Environ. Modell. Software 15 (1): 101–124.
Mamo, T. 2013. “Virtual district meter area municipal water supply pipeline leak detection and classification using advance pattern recognizer multi-class support vector machine for risk-based asset management.” Doctoral dissertation, Dept. of Civil and Urban Engineering, NYU Tandon School of Engineering. https://engineering.nyu.edu/academics/departments/civil-and-urban-engineering.
Mamo, T., I. Juran, and I. Shahrour. 2014. “Virtual DMA municipal water supply pipeline leak detection and classification using advance pattern recognizer multi-class SVM” J. Pattern Recogn. Res. 9 (1): 25–42. https://doi.org/10.13176/11.548.
Mashford, J., D. De Silva, S. Burn, and D. Marney. 2012. “Leak detection in simulated water pipe networks using SVM” Appl. Artif. Intell. 26 (5): 429–444. https://doi.org/10.1080/08839514.2012.670974.
Mohri, M., A. Rostamizadeh, and A. Talwalkar. 2012. Foundations of machine learning. Cambridge, MA: MIT press.
Mounce, S. R., J. B. Boxall, and J. Machell. 2010. “Development and verification of an online artificial intelligence system for detection of bursts and other abnormal flows” J. Water Resour. Plann. Manage. 136 (3): 309–318. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000030.
Mounce, S. R., A. J. Day, A. S. Wood, A. Khan, P. D. Widdop, and J. Machell. 2002. “A neural network approach to burst detection” Water Sci. Technol. 45 (4–5): 237–246. https://doi.org/10.2166/wst.2002.0595.
Mounce, S. R., A. Khan, A. S. Wood, A. J. Day, P. D. Widdop, and J. Machell. 2003. “Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system” Inf. Fusion 4 (3): 217–229. https://doi.org/10.1016/S1566-2535(03)00034-4.
Mounce, S. R., and J. Machell. 2006. “Burst detection using hydraulic data from water distribution systems with artificial neural networks” Urban Water J. 3 (1): 21–31. https://doi.org/10.1080/15730620600578538.
Puust, R., Z. Kapelan, D. Savic, and T. Koppel. 2010. “A review of methods for leak management in pipe networks” Urban Water J. 7 (1): 25–45. https://doi.org/10.1080/15730621003610878.
Romano, M., Z. Kapelan, and D. A. Savi. 2010. “Real-time leak detection in water distribution systems.” In Water distribution systems analysis, 1074–1082. Reston, VA: ASCE.
Romano, M., Z. Kapelan, and D. A. Savi. 2014. “Automated detection of pipe bursts and other events in water distribution systems” J. Water Resour. Plann. Manage. 140 (4): 457–467. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000339.
Rossman, A. 2000. EPANET 2 Users Manual. Cincinnati: USEPA.
US SEC (US Securities and Exchange Commission). 2017. Form 10K for A. Washington, DC: American Water Works Company, Inc.
WUC (Water Utility Council). 2012. “Buried no longer: Confronting America’s water infrastructure challenge.” https://awwa.onlinelibrary.wiley.com/doi/full/10.5942/jawwa.2012.104.0107.
Information & Authors
Information
Published In
Journal of Infrastructure Systems
Volume 26 • Issue 1 • March 2020
Copyright
©2019 American Society of Civil Engineers.
History
Received: Jul 17, 2018
Accepted: Jun 17, 2019
Published online: Dec 26, 2019
Published in print: Mar 1, 2020
Discussion open until: May 26, 2020
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