Technical Papers
Mar 21, 2022

Multiple Leakage Detection and Isolation in District Metering Areas Using a Multistage Approach

Publication: Journal of Water Resources Planning and Management
Volume 148, Issue 6

Abstract

Hydraulic accidents or abnormal situations, also known as leakages, cause not only water losses but also service interruptions and other negative effects. To facilitate the rapid response of water utilities and reduce water losses caused by undiscovered leakages, a timely, efficient, and accurate detection method is required. This paper describes a novel framework for multiple overlapping leak detection and isolation in the water distribution system. The proposed method has three stages: an estimation stage to extract leak-induced data from the huge monitoring data, an identification stage to detect the presence of overlapping leaks from multiscale difference series, and a localization stage to pinpoint the leak hotspots. The proposed method detects seven leakages in real-life networks and localizes 16 overlapping leakages with an economic score of €167,981 in L-town 2019. Comparisons show that the framework can accurately detect it in a fast response time and effectively localize the leaking pipeline with good applicability.

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Data Availability Statement

The case data and the model used in this study can be made available from BattleDIM 2020, and the codes for the proposed method in Spyder3.7 can be made available by the corresponding author by request.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 51879139).

References

Ahn, J., and D. Jung. 2019. “Hybrid statistical process control method for water distribution pipe burst detection.” J. Water Resour. Plann. Manage. 145 (9): 06019008. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001104.
Bakker, M., E. A. Trietsch, J. H. G. Vreeburg, and L. C. Rietveld. 2014a. “Analysis of historic bursts and burst detection in water supply areas of different size.” Water Sci. Technol. Water Supply 14 (6): 1035–1044. https://doi.org/10.2166/ws.2014.063.
Bakker, M., J. H. G. Vreeburg, K. M. V. Schagen, and L. C. Rietveld. 2013. “A fully adaptive forecasting model for short-term drinking water demand.” Environ. Modell. Software 48 (5): 141–151. https://doi.org/10.1016/j.envsoft.2013.06.012.
Bakker, M., J. H. G. Vreeburg, M. Van De Roer, and L. C. Rietveld. 2014b. “Heuristic burst detection method using flow and pressure measurements.” J. Hydroinf. 16 (5): 1194–1209. https://doi.org/10.2166/hydro.2014.120.
Cleveland, R. B., W. S. Cleveland, and I. Terpenning. 1990. “STL: A seasonal-trend decomposition procedure based on loess.” J. Off. Stat. 6 (1): 3.
Farley, B., S. R. Mounce, and J. B. Boxall. 2013. “Development and field validation of a burst localization methodology.” J. Water Resour. Plann. Manage. 139 (6): 604–613. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000290.
Flajolet, P., and R. Sedgewick. 1995. “Mellin transforms and asymptotics: Finite differences and Rice’s integrals.” Theor. Comput. Sci. 144 (1): 101–124. https://doi.org/10.1016/0304-3975(94)00281-M.
Hu, W. F., Y. H. He, Z. Y. Liu, J. R. Tan, M. Yang, and J. C. Chen. 2021. “Toward a digital twin: Time series prediction based on a hybrid ensemble empirical mode decomposition and BO-LSTM neural networks.” J. Mech. Des. 143 (5): 21. https://doi.org/10.1115/1.4048414.
Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu. 1998. “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.” Proc. R. Soc. London, Ser. A 454 (1971): 903–995. https://doi.org/10.1098/rspa.1998.0193.
Hutton, C. J., and Z. Kapelan. 2015. “A probabilistic methodology for quantifying, diagnosing and reducing model structural and predictive errors in short term water demand forecasting.” Environ. Modell. Software 66 (Apr): 87–97. https://doi.org/10.1016/j.envsoft.2014.12.021.
Jung, D., D. Kang, J. Liu, and K. Lansey. 2015. “Improving the rapidity of responses to pipe burst in water distribution systems: A comparison of statistical process control methods.” J. Hydroinf. 17 (2): 307–328. https://doi.org/10.2166/hydro.2014.101.
Jung, D. H., and K. Lansey. 2015. “Water distribution system burst detection using a nonlinear Kalman filter.” J. Water Resour. Plann. Manage. 141 (5): 04014070. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000464.
Kang, Y. F., D. Belusic, and K. Smith-Miles. 2014. “Detecting and classifying events in noisy time series.” J. Atmos. Sci. 71 (3): 1090–1104. https://doi.org/10.1175/JAS-D-13-0182.1.
Karthikeyan, L., and D. Nagesh Kumar. 2013. “Predictability of nonstationary time series using wavelet and EMD based ARMA models.” J. Hydrol. 502 (Oct): 103–119. https://doi.org/10.1016/j.jhydrol.2013.08.030.
Li, R., H. Huang, K. Xin, and T. Tao. 2015. “A review of methods for burst/leakage detection and location in water distribution systems.” Water Sci. Technol. Water Supply 15 (3): 429–441. https://doi.org/10.2166/ws.2014.131.
Machell, J., S. R. Mounce, and J. B. Boxall. 2006. “Development of artificial intelligence systems for analysis of water supply system data.” In Proc., 8th Water Distribution Systems Analysis Symp. Reston, VA: ASCE.
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., R. B. Mounce, and J. B. Boxall. 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.
Mutikanga, H. E., S. K. Sharma, and K. Vairavamoorthy. 2013. “Methods and tools for managing losses in water distribution systems.” J. Water Resour. Plann. Manage. 139 (2): 166–174. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000245.
Romano, M., Z. Kapelan, and D. A. Savic. 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.
Sanz, G., R. Perez, Z. Kapelan, and D. Savic. 2016. “Leak detection and localization through demand components calibration.” J. Water Resour. Plann. Manage. 142 (2): 04015057. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000592.
Savic, D. A., Z. S. Kapelan, and P. M. R. Jonkergouw. 2009. “Quo Vadis water distribution model calibration?” Urban Water J. 6 (1): 3–22. https://doi.org/10.1080/15730620802613380.
Shimanskiy, S., T. Iijima, and Y. Naoi. 2003. “Development of microphone leak detection technology on Fugen NPP.” Prog. Nucl. Energy 43 (1): 357–364. https://doi.org/10.1016/S0149-1970(03)00043-X.
Steffelbauer, D., M. Neumayer, M. Gunther, and D. Fuchs-Hanusch. 2014. “Sensor placement and leakage localization considering demand uncertainties.” In Proc., 16th Int. Conf. on Water Distribution System Analysis (WDSA), 1160–1167. Amsterdam, Netherlands: Elsevier. https://doi.org/10.1016/j.proeng.2014.11.242.
Wang, X., G. Guo, S. Liu, Y. Wu, X. Xu, and K. Smith. 2020. “Burst detection in district metering areas using deep learning method.” J. Water Resour. Plann. Manage. 146 (6): 04020031. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001223.
Wu, Y. P., and S. M. Liu. 2017a. “Clustering-based burst detection using multiple pressure sensors in district metering areas.” In Proc., Int. Computing and Control for the Water Industry Conf. Sheffield, UK: Univ. of Sheffield.
Wu, Y. P., and S. M. Liu. 2017b. “A review of data-driven approaches for burst detection in water distribution systems.” Urban Water J. 14 (9): 972–983. https://doi.org/10.1080/1573062X.2017.1279191.
Wu, Y. P., S. M. Liu, K. Smith, and X. T. Wang. 2018. “Using correlation between data from multiple monitoring sensors to detect bursts in water distribution systems.” J. Water Resour. Plann. Manage. 144 (2): 04017084. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000870.
Wu, Z. Y. 2009. “Unified parameter optimisation approach for leakage detection and extended-period simulation model calibration.” Urban Water J. 6 (1): 53–67. https://doi.org/10.1080/15730620802541631.
Wu, Z. Y., P. Sage, and D. Turtle. 2010. “Pressure-dependent leak detection model and its application to a district water system.” J. Water Resour. Plann. Manage. 136 (1): 116–128. https://doi.org/10.1061/(ASCE)0733-9496(2010)136:1(116).
Xie, X., D. Hou, X. Tang, and H. Zhang. 2019. “Leakage identification in water distribution networks with error tolerance capability.” Water Resour. Manage. 33 (3): 1233–1247. https://doi.org/10.1007/s11269-018-2179-y.
Xu, Q., Q. W. Chen, J. F. Ma, and K. Blanckaert. 2013. “Optimal pipe replacement strategy based on break rate prediction through genetic programming for water distribution network.” J. Hydro-environ. Res. 7 (2): 134–140. https://doi.org/10.1016/j.jher.2013.03.003.
Xu, Q., Z. M. Qiang, Q. W. Chen, K. Liu, and N. Cao. 2018. “A superposed model for the pipe failure assessment of water distribution networks and uncertainty analysis: A case study.” Water Resour. Manage. 32 (5): 1713–1723. https://doi.org/10.1007/s11269-017-1899-8.
Xu, W., X. Zhou, K. Xin, J. Boxall, H. Yan, and T. Tao. 2020. “Disturbance extraction for burst detection in water distribution networks using pressure measurements.” Water Resour. Res. 56 (5): e2019WR025526. https://doi.org/10.1029/2019WR025526.
Ye, G., and R. A. Fenner. 2014. “Study of burst alarming and data sampling frequency in water distribution networks.” J. Water Resour. Plann. Manage. 140 (6): 06014001. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000394.
Ye, G. L., and R. A. Fenner. 2011. “Kalman filtering of hydraulic measurements for burst detection in water distribution systems.” J. Pipeline Syst. Eng. Pract. 2 (1): 14–22. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000070.
Zhou, X., Z. Tang, W. Xu, F. Meng, X. Chu, K. Xin, and G. Fu. 2019. “Deep learning identifies accurate burst locations in water distribution networks.” Water Res. 166 (Dec): 115058. https://doi.org/10.1016/j.watres.2019.115058.

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Go to Journal of Water Resources Planning and Management
Journal of Water Resources Planning and Management
Volume 148Issue 6June 2022

History

Received: May 16, 2021
Accepted: Jan 26, 2022
Published online: Mar 21, 2022
Published in print: Jun 1, 2022
Discussion open until: Aug 21, 2022

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Authors

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Xiaoting Wang [email protected]
Ph.D. Student, Smart Water Research Center, School of Environment, Tsinghua Univ., Beijing 100084, China. Email: [email protected]
Ph.D. Student, Smart Water Research Center, School of Environment, Tsinghua Univ., Beijing 100084, China. Email: [email protected]
Professor, Smart Water Research Center, School of Environment, Tsinghua Univ., Beijing 100084, China (corresponding author). ORCID: https://orcid.org/0000-0002-4949-4318. Email: [email protected]
Ph.D. Student, Smart Water Research Center, School of Environment, Tsinghua Univ., Beijing 100084, China. Email: [email protected]
Master’s Student, Smart Water Research Center, School of Environment, Tsinghua Univ., Beijing 100084, China. Email: [email protected]

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Cited by

  • Locating Multiple Leaks in Water Distribution Networks Combining Physically Based and Data-Driven Models and High-Performance Computing, Journal of Water Resources Planning and Management, 10.1061/JWRMD5.WRENG-6005, 149, 12, (2023).
  • Comparison of AMI and SCADA Systems for Leak Detection and Localization in Water Distribution Networks, Journal of Water Resources Planning and Management, 10.1061/JWRMD5.WRENG-5953, 149, 11, (2023).
  • Linear Programming Models for Leak Detection and Localization in Water Distribution Networks, Journal of Water Resources Planning and Management, 10.1061/JWRMD5.WRENG-5720, 149, 5, (2023).
  • Leakage detection in water distribution networks via 1D CNN deep autoencoder for multivariate SCADA data, Engineering Applications of Artificial Intelligence, 10.1016/j.engappai.2023.106062, 122, (106062), (2023).
  • Battle of the Leakage Detection and Isolation Methods, Journal of Water Resources Planning and Management, 10.1061/(ASCE)WR.1943-5452.0001601, 148, 12, (2022).

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