A Real-Time Method to Detect the Leakage Location in Urban Water Distribution Networks
Publication: Journal of Water Resources Planning and Management
Volume 148, Issue 12
Abstract
Water distribution networks (WDNs) are prone to infrastructure failures that lead to water loss. This paper presents a practical and efficient leakage detection and localization method based on the supervisory control and data acquisition (SCADA) system combined with state estimation. In WDNs, normal node demand patterns, seasonal variations, and leakage can cause pressure sensor values to fluctuate; accordingly, the pressure sensor values in a SCADA system can be regarded as exhibiting a mixture of normal and abnormal fluctuations. To obtain the fluctuation signal arising from leakage, the node demands must first be calibrated. For leakage detection, the proposed method involves comparing the errors between historical monitored values and simulated values to assess whether a change in these errors is due to normal system changes or the impact of leakage. At the leakage location, an emitter coefficient is used to simulate unknown leakage flow. Then, similarity indicators of the leakage locations are constructed using the Euclidian distance, Pearson correlation coefficient, and Jaccard similarity. The leakage location can be determined by comparing these similarity values. The performance of this method was tested on the L-Town WDN provided in the BattLeDIM competition. Our model’s ability to quickly and accurately detect and locate leakages was validated on the 2018 data set of leakage events. The model then was applied to predict the times and locations of leakages in 2019, and eight leakages were detected, six of which were true positives.
Practical Applications
This paper presents a practical and efficient leakage detection and localization method based on the SCADA system combined with state estimation. At leakage detection, the main steps include real-time demand calibration, difference analysis and threshold setting between simulated value and historical monitoring value, and leakage detection judgment. At the leakage location, the main steps include calculating the change of pressure value after adding a leakage, selecting the pressure sensor with the largest change, applying clustering algorithm to narrow the location scope, leakage simulation, similarity analysis, and leakage location. The performance of the method was tested on the L-Town WDN with a population of about 10,000 and about 800 pipes. Based on the SCADA data for 2019, we identified 8 pipe leakage events in total—6 true positives and 2 false positives—and 17 false negatives. This method has the potential to be applied in other cities. The data required for this method include SCADA data, historical leakage reports, and the water distribution network with distributed node demand. The method assumes that there is only one leakage at a time in the network. When multiple leakages occur simultaneously, the reliability of detection results is affected.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (61662045) and the Special Program of talents Development for Excellent Youth Scholars in Tianjin. Xiaoyu Ma and Yalin Li contributed equally to this work.
References
Abdulshaheed, A., F. Mustapha, and A. Ghavamian. 2017. “A pressurebased method for monitoring leaks in a pipe distribution system: A review.” Renewable Sustainable Energy Rev. 69 (Mar): 902–911. https://doi.org/10.1016/j.rser.2016.08.024.
Aksela, K., M. Aksela, and R. Vahala. 2009. “Leakage detection in a real distribution network using a SOM.” Urban Water J. 6 (4): 279–289. https://doi.org/10.1080/15730620802673079.
Bohorquez, J., A. R. Simpson, M. F. Lambert, and B. Alexander. 2021. “Merging fluid transient waves and artificial neural networks for burst detection and identification in pipelines.” J. Water Resour. Plann. Manage. 147 (1): 04020097. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001296.
Buchberger, S. G., and G. Nadimpalli. 2004. “Leak estimation in water distribution systems by statistical analysis of flow readings.” J. Water Resour. Plann. Manage. 130 (4): 321–329. https://doi.org/10.1061/(ASCE)0733-9496(2004)130:4(321).
Cheng, W., Y. Chen, and G. Xu. 2020. “Optimizing sensor placement and quantity for pipe burst detection in a water distribution network.” J. Water Resour. Plann. Manage. 146 (11): 04020088. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001298.
Colombo, A. F., and B. W. Karney. 2002. “Energy and costs of leaky pipes: Toward comprehensive picture.” J. Water Resour. Plann. Manage. 128 (6): 441–450. https://doi.org/10.1061/(ASCE)0733-9496(2002)128:6(441).
Eliades, D. G., M. Kyriakou, S. Vrachimis, and M. M. Polycarpou. 2017. “EPANET-MATLAB Toolkit: An open-source software for interfacing EPANET with MATLAB.” In Proc., Critical Information Infrastructures Security. Amsterdam, Netherlands: Computing and Control for the Water Industry. https://doi.org/10.5281/zenodo.831493.
Farley, B., S. R. Mounce, and J. B. Boxall. 2011. “Field validation of ‘optimal’ instrumentation methodology for burst/leak detection and location.” In Water distribution systems analysis 2010, 1093–1102. Reston, VA: ASCE. https://doi.org/10.1061/41203(425)99.
Goulet, J.-A., S. Coutu, and I. F. C. Smith. 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.
Guo, S., T. Zhang, W. Shao, D. Z. Zhu, and Y. Duan. 2013. “Two-dimensional pipe leakage through a line crack in water distribution systems.” J. Zhejiang Univ. Sci. A 14 (5): 371–376. https://doi.org/10.1631/jzus.A1200227.
Hagos, M., D. Jung, and K. E. Lansey. 2016. “Optimal meter placement for pipe burst detection in water distribution systems.” J. Hydroinf. 18 (4): 741–756. https://doi.org/10.2166/hydro.2016.170.
Hill, M. C. 1998. Methods and guidelines for effective model calibration.. Washington, DC: USGS.
Kumar, M., S. Chatterjee, W. Zhang, J. Yang, and L. M. Kolbe. 2019. “Fuzzy theoretic model based analysis of image features.” Inf. Sci. 480 (Apr): 34–54. https://doi.org/10.1016/j.ins.2018.12.024.
Kumar, M., and B. Freudenthaler. 2019. “Fuzzy membership functional analysis for nonparametric deep models of image features.” IEEE Trans. Fuzzy Syst. 28 (12): 3345–3359. https://doi.org/10.1109/TFUZZ.2019.2950636.
Kumar, M., Y. Mao, Y. Wang, T. Qiu, Y. Chenggen, and W. Zhang. 2017. “Fuzzy theoretic approach to signals and systems: Static systems.” Inf. Sci. 418 (Dec): 668–702. https://doi.org/10.1016/j.ins.2017.08.048.
Kumar, M., W. Zhang, M. Weippert, and B. Freudenthaler. 2020. “An explainable fuzzy theoretic nonparametric deep model for stress assessment using heartbeat intervals analysis.” IEEE Trans. Fuzzy Syst. 29 (12): 3873–3886. https://doi.org/10.1109/TFUZZ.2020.3029284.
Kun, D., T.-Y. Long, J.-H. Wang, and J.-S. Guo. 2015. “Inversion model of water distribution systems for nodal demand calibration.” J. Water Resour. Plann. Manage. 141 (9): 04015002. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000506.
Lee, S. J., G. Lee, J. C. Suh, and J. M. Lee. 2016. “Online burst detection and location of water distribution systems and its practical applications.” J. Water Resour. Plann. Manage. 142 (1): 04015033. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000545.
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.
Maier, H., et al. 2014. “Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions.” Environ. Modell. Software 62 (Dec): 271–299. https://doi.org/10.1016/j.envsoft.2014.09.013.
Mounce, S., J. 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.
Nasirian, A., M. Maghrebi, and S. Yazdani. 2013. “Leakage detection in water distribution network based on a new heuristic genetic algorithm model.” J. Water Resour. Prot. 5 (3): 294–303. https://doi.org/10.4236/jwarp.2013.53030.
Palau, C. V., F. J. Arregui, and M. Carlos. 2012. “Burst detection in water networks using principal component analysis.” J. Water Resour. Plann. Manage. 138 (1): 47–54. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000147.
Puust, R., Z. Kapelan, D. A. Savic, and T. Koppel. 2010. “A review of methods for leakage management in pipe networks.” Urban Water J. 7 (1): 25–45. https://doi.org/10.1080/15730621003610878.
Qi, Z., F. Zheng, D. Guo, H. R. Maier, T. Zhang, T. Yu, and Y. Shao. 2018. “Better understanding of the capacity of pressure sensor systems to detect pipe burst within water distribution networks.” J. Water Resour. Plann. Manage. 144 (7): 04018035. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000957.
Romano, M., Z. Kapelan, and D. A. Savić. 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. Pérez, Z. Kapelan, and D. Savic. 2016. “Leak detection and localization through demand components calibration.” J. Water Resour. Plann. Manage. 142 (2): 1097–1098. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000592.
Sophocleous, S., D. Savić, and Z. Kapelan. 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.
Stephens, M., J. Gong, C. Zhang, A. Marchi, L. Dix, and M. F. Lambert. 2020. “Leak-before-break main failure prevention for water distribution pipes using acoustic smart water technologies: Case study in Adelaide.” J. Water Resour. Plann. Manage. 146 (10): 05020020. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001266.
Sumer, D., and K. Lansey. 2009. “Effect of uncertainty on water distribution system model design decisions.” J. Water Resour. Plann. Manage. 135 (1): 38–47. https://doi.org/10.1061/(ASCE)0733-9496(2009)135:1(38).
Vrachimis, S. G., D. G. Eliades, R. Taormina, Z. Kapelan, A. Ostfeld, S. Liu, M. Kyriakou, P. Pavlou, M. Qiu, and M. M. Polycarpou. 2022. “Battle of the leakage detection and isolation methods.” J. Water Resour. Plann. Manage. 148 (12). https://doi.org/10.1061/(ASCE)WR.1943-5452.0001601.
Zeng, W., A. C. Zecchin, B. S. Cazzolato, A. R. Simpson, J. Gong, and M. F. Lambert. 2021. “Extremely sensitive anomaly detection in pipe networks using a higher-order paired-impulse response function with a correlator.” J. Water Resour. Plann. Manage. 147 (10): 04021068. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001446.
Zhang, Q., Z. Y. Wu, M. Zhao, J. Qi, Y. Huang, and H. Zhao. 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.
Zhang, W., Y. Mao, M. Kumar, Y. Li, and J. Liu. 2018. “A new method for on-line demand calibration of WDS hydraulic model.” Desalin. Water Treat. 121 (2018): 111–117. https://doi.org/10.5004/dwt.2018.22374.
Zhou, Z., C. Hu, D. Xu, J. Yang, and D. Zhou. 2011. “Bayesian reasoning approach based recursive algorithm for online updating belief rule based expert system of pipeline leak detection.” Expert Syst. Appl. 38 (4): 3937–3943. https://doi.org/10.1016/j.eswa.2010.09.055.
Information & Authors
Information
Published In
Copyright
© 2022 American Society of Civil Engineers.
History
Received: May 5, 2021
Accepted: Aug 1, 2022
Published online: Oct 6, 2022
Published in print: Dec 1, 2022
Discussion open until: Mar 6, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Xi Wan, Raziyeh Farmani, Edward Keedwell, Gradual Leak Detection in Water Distribution Networks Based on Multistep Forecasting Strategy, Journal of Water Resources Planning and Management, 10.1061/JWRMD5.WRENG-6001, 149, 8, (2023).
- Ridwan Taiwo, Ibrahim Abdelfadeel Shaban, Tarek Zayed, Development of sustainable water infrastructure: A proper understanding of water pipe failure, Journal of Cleaner Production, 10.1016/j.jclepro.2023.136653, 398, (136653), (2023).
- Hoese Michel Tornyeviadzi, Razak Seidu, 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).