Machine Learning–Assisted Model for Leak Detection in Water Distribution Networks Using Hydraulic Transient Flows
Publication: Journal of Water Resources Planning and Management
Volume 148, Issue 2
Abstract
This study introduces a novel hybrid leak detection method based on machine learning (ML) and hydraulic transient modeling for pipe networks. First, the transient hydraulic simulation model is developed in the time domain. Then, the optimum measurement sites for sampling the network’s hydraulic responses are determined using a graph-based method. The model exploits the network’s high-frequency transient responses at measurement sites to generate data sets. The generated samples are transformed into the frequency domain using the fast Fourier transform (FFT). The neighborhood component analysis (NCA) is used for feature selection and the optimum classifier is selected by comparing the performance of different classification algorithms. The model is finally applied to two case studies: an experimental reservoir-pipe-valve (RPV) system and a complex water distribution network (WDN). The accuracy of leak detection is evaluated considering fast and slow transient excitations concerning various levels of uncertainty in the system parameters. The results indicated that the model could detect leaks accurately and is stable and reliable against high uncertainties in pipe friction factors and nodal demands.
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Data Availability Statement
The simulation and experimental data as used during the study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to thank the corresponding Associate Editor and anonymous reviewers for their constructive comments and suggestions.
References
Abouhamad, M., T. Zayed, and O. Moselhi. 2016. “Leak detection in buried pipes using ground penetrating radar; a comparative study.” In Pipelines 2016, 417–424. Reston, VA: ASCE.
Al-Tofan, M., et al. 2019. “Use of lower harmonics of pressure oscillations for blockage detection in liquid pipelines.” J. Hydraul. Eng. 145 (3): 04018090. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001568.
Al-Tofan, M., et al. 2020. “Leak detection in liquid pipelines using lower harmonics of pressure oscillations.” J. Pipeline Syst. Eng. Pract. 11 (4): 04020033. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000479.
Ayati, A., H., Haghighi, and P., Lee. 2019. “Statistical review of major standpoints in hydraulic transient-based leak detection.” J. Hydraul. Struct. 5 (1): 1–26. https://doi.org/10.22055/jhs.2019.27926.1095.
Bohorquez, J., et al. 2020. “Leak detection and topology identification in pipelines using fluid transients and artificial neural networks.” J. Water Resour. Plann. Manage. 146 (6): 04020040. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001187.
Brunone, B., et al. 1995. “Effects of two-dimensionality on pipe transients modeling.” J. Hydraul. Eng. 121 (12): 906–912. https://doi.org/10.1061/(ASCE)0733-9429(1995)121:12(906).
Burges, C. J. C. 1998. “A tutorial on support vector machines for pattern recognition.” Data Mining Knowl. Discovery 2 (2): 121–167. https://doi.org/10.1023/A:1009715923555.
Butterworth, S. 1930. “On the theory of filter amplifiers.” Exp. Wireless Eng. 7 (6): 536–541.
Caputo, A. C., and P. M. Pelagagge. 2003. “Using neural networks to monitor piping systems.” Process Saf. Prog. 22 (2): 119–127. https://doi.org/10.1002/prs.680220208.
Chaudhry, M. H. 2016. Applied hydraulic transients. New York: Springer.
Chunli, F., S. Fengrui, and Y. Li. 2005. “Investigation on nondestructive evaluation of pipelines using infrared thermography.” In Proc., 2005 Joint 30th Int. Conf. on Infrared and Millimeter Waves and 13th Int. Conf. on Terahertz Electronics. New York: IEEE.
Duan, H. F., and P. J. Lee. 2016. “Transient-based frequency domain method for dead-end side branch detection in reservoir pipeline-valve systems.” J. Hydraul. Eng. 142 (2): 04015042. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001070.
Farley, B., et al. 2011. “Field validation of ‘optimal’ instrumentation methodology for burst/leak detection and location.” In Proc., Water Distribution Systems Analysis Conf. Reston, VA: ASCE.
Feng, J., and H. Zhang. 2006. “Algorithm of pipeline leak detection based on discrete incremental clustering method.” In Computational intelligence. Berlin: Springer.
Ferrante, M., et al. 2009. “Leak-edge detection.” J. Hydraul. Res. 47 (2): 233–241. https://doi.org/10.3826/jhr.2009.3220.
Ferrante, M., and B. Brunone. 2003. “Pipe system diagnosis and leak detection by unsteady-state tests. 1. Harmonic analysis.” Adv. Water Resour. 26 (1): 95–105. https://doi.org/10.1016/S0309-1708(02)00101-X.
Fisher, R. A. 1936. “The use of multiple measurements in taxonomic problems.” Ann. Eugenics 7 (2): 179–188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x.
Fix, E., and J. L. Hodges. 1951. Discriminatory analysis, nonparametric discrimination: Consistency properties. SSan Antonio, TX: USAF School of Aviation Medicine.
Goulet, J.-A., et al. 2013. “Model falsification diagnosis and sensor placement for leak detection in pressurized pipe networks.” Adv. Eng. Inf. 27 (2): 261–269. https://doi.org/10.1016/j.aei.2013.01.001.
Gupta, G. 2017. “Monitoring water distribution network using machine learning.” Master thesis, KTH, School of Electrical Engineering and Computer Science. https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-221832.
Haghighi, A., and A. Keramat. 2012. “A fuzzy approach for considering uncertainty in transient analysis of pipe networks.” J. Hydroinf. 14 (4): 1024–1035. https://doi.org/10.2166/hydro.2012.191.
Haghighi, A., and H. M. Ramos. 2012. “Detection of leakage freshwater and friction factor calibration in drinking networks using central force optimization.” Water Resour. Manage. 26 (8): 2347–2363. https://doi.org/10.1007/s11269-012-0020-6.
Jung, B. S., and B. W. Karney. 2008. “Systematic exploration of pipeline network calibration using transients.” J. Hydraul. Res. 46 (1): 129–137. https://doi.org/10.1080/00221686.2008.9521947.
Khilqa, S., et al. 2019a. “Damping in transient pressurized flows.” J. Hydraul. Eng. 145 (10): 04019034. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001624.
Khilqa, S., et al. 2019b. “Uncertainty quantification for damping in transient pressure oscillations.” J. Water Resour. Plann. Manage. 145 (9): 04019039. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001089.
Kim, S. H. 2018. “Development of multiple leakage detection method for a reservoir pipeline valve system.” Water Resour. Manage. 32 (6): 2099–2112. https://doi.org/10.1007/s11269-018-1920-x.
Lee, P. J., et al. 2005. “Frequency domain analysis for detecting pipeline leaks.” J. Hydraul. Eng. 131 (7): 596–604. https://doi.org/10.1061/(ASCE)0733-9429(2005)131:7(596).
Lee, P. J., et al. 2015. “Numerical and experimental study on the effect of signal bandwidth on pipe assessment using fluid transients.” J. Hydraul. Eng. 141 (2): 04014074. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000961.
Leu, S.-S., and Q.-N. Bui. 2016. “Leak prediction model for water distribution networks created using a Bayesian network learning approach.” Water Resour. Manage. 30 (8): 2719–2733. https://doi.org/10.1007/s11269-016-1316-8.
Liggett, A., and L. C. Chen. 1994. “Inverse transient analysis in pipe networks.” J. Hydraul. Eng. 120 (8): 934–955. https://doi.org/10.1061/(ASCE)0733-9429(1994)120:8(934).
Mashford, J., et al. 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.
Mashford, J., D. D. Silva, D. Marney, and S. Burn. 2009. “An approach to leak detection in pipe networks using analysis of monitored pressure values by support vector machine.” In Proc., 2009 3rd Int. Conf. on Network and System Security, 534–539. New York: IEEE.
Mounce, S. R., et al. 2011. “Novelty detection for time series data analysis in water distribution systems using support vector machines.” J. Hydroinf. 13 (4): 672–686. https://doi.org/10.2166/hydro.2010.144.
Ozevin, D., and J. Harding. 2012. “Novel leak localization in pressurized pipeline networks using acoustic emission and geometric connectivity.” Int. J. Press. Vessels Pip. 63–69. https://doi.org/101016/jijpvp201201001.
Plath, M., et al. 2014. “Energy efficiency and energy saving in the German water industry.” Water Pract. Technol. 9 (2): 256–263. https://doi.org/10.2166/wpt.2014.029.
Poulakis, Z., et al. 2003. “Leakage detection in water pipe networks using a Bayesian probabilistic framework.” Probab. Eng. Mech. 18 (4): 315–327. https://doi.org/10.1016/S0266-8920(03)00045-6.
Pudar, R. S., and J. A. Liggett. 1992. “Leaks in pipe networks.” J. Hydraul. Eng. 118 (7): 1031–1046. https://doi.org/10.1061/(ASCE)0733-9429(1992)118:7(1031).
Raghu, S., and N. Sriraam. 2018. “Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms.” Expert Syst. Appl. 113 (Dec): 18–32. https://doi.org/10.1016/j.eswa.2018.06.031.
Ranginkaman, M. H., et al. 2019. “Frequency domain modelling of pipe transient flow with the virtual valves method to reduce linearization errors.” Mech. Syst. Sig. Process. 131 (Sep): 486–504. https://doi.org/10.1016/j.ymssp.2019.05.065.
Ranginkaman, M. H., A. Haghighi, and H. M. Vali Samani. 2016. “Inverse frequency response analysis for pipelines leak detection using the particle swarm optimization.” Int. J. Optim. Civ. Eng. 6 (1): 1–12.
Rossman, L. A. 1994. EPANET users manual: Project summary. Cincinnati: USEPA.
Sabzkouhi, A. M., and A. Haghighi. 2018. “Uncertainty analysis of transient flow in water distribution networks.” Water Resour. Manage. 32 (12): 3853–3870. https://doi.org/10.1007/s11269-018-2023-4.
Sarkamaryan, S. 2020. “Development of a meta-model for inverse transient analysis of water supply networks for leak detection and calibration.” Ph.D. thesis, Water Resource Management and Engineering, Univ. of Ahvaz.
Sarkamaryan, S., et al. 2020. “Surrogate-assisted inverse transient analysis (SAITA) for leakage detection in pressurized piping systems.” Iran. J. Sci. Technol. Trans. Civ. Eng. 45 (4): 2707–2718. https://doi.org/10.1007/s40996-020-00516-4.
Shamloo, H., and A. Haghighi. 2010. “Optimum leak detection and calibration of pipe networks by inverse transient analysis.” J. Hydraul. Res. 48 (3): 371–376. https://doi.org/10.1080/00221681003726304.
Soldevila, A., et al. 2017. “Leak localization in water distribution networks using Bayesian classifiers.” J. Process Control 55 (Jul): 1–9. https://doi.org/10.1016/j.jprocont.2017.03.015.
Sophocleous, S., et al. 2019. “Leak localization in a real water distribution network based on search-space reduction.” J. Water Resour. Plann. Manage. 145 (7): 04019024. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001079.
Tang, X., Y. Liu, L. Zheng, C. Ma, and H. Wang. 2009. “Leak detection of water pipeline using wavelet transform method.” In Proc., Int. Conf. on Environmental Science and Information Application Technology, 217–220. New York: IEEE. https://doi.org/10.1109/ESIAT.2009.57.
Tao, T., et al. 2014. “Burst detection using an artificial immune network in water-distribution Systems.” J. Water Resour. Plann. Manage. 140 (10): 04014027. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000405.
Vitkovsky, J., M. Lambert, and A. Simpson. 2000. “Advances in unsteady friction modelling in transient pipe flow.” In Proc., 8th Int. Conf. on Pressure Surges. London: Professional Engineering Publishing Limited.
Vitkovsky John, P., et al. 2000. “Leak detection and calibration using transients and genetic algorithms.” J. Water Resour. Plann. Manage. 126 (4): 262–265. https://doi.org/10.1061/(ASCE)0733-9496(2000)126:4(262).
Walt, J. C., et al. 2019. “Pipe network leak detection: Comparison between statistical and machine learning techniques.” Urban Water J. 15 (10): 953–960. https://doi.org/10.1080/1573062X.2019.1597375.
Wang, X.-J., et al. 2002. “Leak detection in pipelines using the damping of fluid transients.” J. Hydraul. Eng. 128 (7): 697–711. https://doi.org/10.1061/(ASCE)0733-9429(2002)128:7(697).
Xu, X., and B. Karney. 2017. “An overview of transient fault detection techniques.” In Modeling and monitoring of pipelines and networks: Advanced tools for automatic monitoring and supervision of pipelines, 13–37. Berlin: Springer.
Yang, W., K. Wang, and W. Zuo. 2012. “Neighborhood component feature selection for high-dimensional data.” J. Comput. 7 (1): 161–168. https://doi.org/10.4304/jcp.7.1.161-168.
Zeng, W., et al. 2020. “Paired-IRF method for detecting leaks in pipe networks.” J. Water Resour. Plann. Manage. 146 (5): 04020021. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001193.
Zhang, Q., et al. 2016. “Leakage zone identification in large-scale water distribution systems using multiclass support vector machines.” J. Water Resour. Plann. Manage. 142 (11): 04016042. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000661.
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Published In
Journal of Water Resources Planning and Management
Volume 148 • Issue 2 • February 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Feb 19, 2021
Accepted: Oct 13, 2021
Published online: Dec 13, 2021
Published in print: Feb 1, 2022
Discussion open until: May 13, 2022
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Flow (fluid dynamics)
- Fluid dynamics
- Fluid mechanics
- Hydraulic engineering
- Hydraulic models
- Hydraulic networks
- Hydraulic structures
- Hydraulic transients
- Hydrologic engineering
- Infrastructure
- Models (by type)
- Pipe leakage
- Pipeline management
- Pipeline systems
- Transient flow
- Water and water resources
- Water leakage and water loss
- Water management
- Water supply
- Water supply systems
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