Leakage Identification in Water Distribution Networks Based on XGBoost Algorithm
Publication: Journal of Water Resources Planning and Management
Volume 148, Issue 3
Abstract
To detect leakage in urban water distribution networks and study the relationship between monitoring information and leakage diagnosis, the XGBoost algorithm was applied to identify the leakage zone and predict the leakage level. Software was adopted to call EPANET V2.2 for analyzing a water distribution network model, and emitters were added in the middle of pipes to simulate leakage events. By changing the discharge coefficient, the leakage flow was varied in hydraulic analysis. The node pressure sensitivity matrix was calculated, and the sensor placement and pipe zones were determined using the fuzzy -means clustering method. Different leakage scenarios were simulated, and the location and level of leakage were identified through pressure changes at monitoring points based on the XGBoost algorithm. Taking two hydraulic models of water distribution network as examples to simulate and predict, which were compared with back-propagation neural network algorithm, it was revealed that the XGBoost algorithm can not only identify the leakage zone, but also predict the leakage level well. On the basis of sensor placement by Fuzzy c-means algorithm, for different leak scenarios, the average identification accuracy of the XGBoost algorithm was 5.54% higher than that of the back-propagation neural network algorithm in leakage zone. The average prediction accuracy of the XGBoost algorithm was 2.71% higher than that of the back-propagation neural network algorithm in leakage level. The XGBoost algorithm effectively can identify the leakage of water distribution networks.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request: (1) the inp file of the virtual pipe network model; (2) the inp files of five leakage scenarios in the virtual pipe network; (3) MATLAB code of sensor placement based on the FCM clustering algorithm; and (4) MATLAB code of leakage prediction based on the BPNN.
Acknowledgments
This work is financially supported by the National Natural Science Foundation of China (Grant No. 51678017).
References
Bezdek, J. C., R. Ehrlich, and W. Full. 1984. “FCM: The fuzzy -means clustering algorithm.” Comput. Geosci. 10 (2–3): 191–203. https://doi.org/10.1016/0098-3004(84)90020-7.
Cao, X. 2013. The study of soft computing for the complex water supply pipe network leakage fault diagnosis based on hydraulic transients. Baoding, China: Agricultural Univ. of Hebei.
Chen, T., and C. Guestrin. 2016. “XGBoost: A scalable tree boosting system.” In Proc., 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 785–794. New York: Association for Computing Machinery.
Costanzo, F., A. F. Morosini, P. Veltri, and C. D. Savi. 2014. “Model calibration as a tool for leakage identification in WDS: A real case study.” Procedia Eng. 89 (Jan): 672–678. https://doi.org/10.1016/j.proeng.2014.11.493.
Devan, P., and N. Khare. 2020. “An efficient XGBoost–DNN-based classification model for network intrusion detection system.” Neural Comput. Appl. 32 (16): 12499–12514. https://doi.org/10.1007/s00521-020-04708-x.
Friedman, J., T. Hastie, and R. Tibshirani. 2000. “Additive logistic regression: A statistical view of boosting.” Ann. Stat. 28 (2): 337–407. https://doi.org/10.1214/aos/1016218223.
Friedman, J. H. 2001. “Greedy function approximation: A gradient boosting machine.” Ann. Stat. 29 (5): 1189–1232. https://doi.org/10.1214/aos/1013203451.
Han, Z., D. Ma, B. Hou, and W. Wang. 2019. “Post-earthquake hydraulic analyses of urban water supply network based on pressure drive demand model.” Sci. Sin. 49 (3): 351–362. https://doi.org/10.1360/N092017-00429.
Hou, B. 2014. Seismic performance assessment and performance-based design of water distribution system. Beijing: Beijing Univ. of Technology.
Jung, D., D. Kang, J. Liu, and K. Lansey. 2013. “Improving resilience of water distribution system through burst detection.” In Proc., 2013 World Environmental and Water Resources Congress, 768–776. Reston, VA: ASCE.
Kapelan, Z. S., D. A. Savic, and G. A. Walters. 2003. “A hybrid inverse transient model for leakage detection and roughness calibration in pipe networks.” J. Hydraul. Res. 41 (5): 481–492. https://doi.org/10.1080/00221680309499993.
Kapelan, Z. S., D. A. Savic, and G. A. Walters. 2005. “Optimal sampling design methodologies for water distribution model calibration.” J. Hydraul. Eng. 131 (3): 190–200. https://doi.org/10.1061/(ASCE)0733-9429(2005)131:3(190).
Li, J. 2005. Earthquake resistance of lifeline engineering. 1st ed. Beijing: Science Press.
Liggett, J. 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).
Lin, Y. 2013. Design of real-time monitoring system for municipal water supply network and optimization of monitoring points. Guangzhou, China: South China Univ. of Technology.
Liu, W., Z. Song, M. Ouyang, and J. Li. 2020. “Recovery-based seismic resilience enhancement strategies of water distribution networks.” Reliab. Eng. Syst. Saf. 203 (Nov): 107088. https://doi.org/10.1016/j.ress.2020.107088.
Liu, W., Y. Zhao, and J. Li. 2015. “Seismic functional reliability analysis of water distribution networks.” Struct. Infrastruct. Eng. 11 (3): 363–375. https://doi.org/10.1080/15732479.2014.887121.
Miao, H., W. Liu, and J. Li. 2020. “Seismic reliability analysis of water distribution networks on the basis of the probability density evolution method.” Struct. Saf. 86 (Sep): 101960. https://doi.org/10.1016/j.strusafe.2020.101960.
Misiunas, D., M. Lambert, A. Simpson, and G. Olsson. 2005. “Burst detection and location in water distribution networks.” Water Supply 5 (3–4): 71–80. https://doi.org/10.2166/ws.2005.0085.
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. 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.
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.
Romano, M., Z. Kapelan, and D. A. Savi. 2011. “Real-time leak detection in water distribution systems.” In Proc., 12th Water Distribution Systems Analysis. Reston, VA: ASCE.
Rossman, L. A. 2020. “EPANET 2.2 online user’s manual.” Accessed October 28, 2020. https://epanet22.readthedocs.io/en/latest/.
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): 04015057. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000592.
Snider, B., and E. A. McBean. 2020. “Watermain breaks and data: The intricate relationship between data availability and accuracy of predictions.” Urban Water J. 17 (2): 163–176. https://doi.org/10.1080/1573062X.2020.1748664.
Steffelbauer, D. B., and D. Fuchs-Hanusch. 2016. “Efficient sensor placement for leak localization considering uncertainties.” Water Resour. Manage. 30 (14): 5517–5533. https://doi.org/10.1007/s11269-016-1504-6.
Vítkovský, J. P., P. J. Lee, M. L. Stephens, M. F. Lambert, and J. A. Liggett. 2003. “Leak and blockage detection in pipelines via an impulse response method.” In Pumps, electromechanical devices and systems. Lisse, Netherelands: A.A. Balkema.
Wang, W., Z. Xi, M. Liu, and X. Wu. 2012. “Optimized arrangement of fault-monitor sensors of water supply network by improved genetic algorithm.” [In Chinese.] J. Hydrol. Eng. 31 (1): 15–19.
Wang, X.-J., M. F. Lambert, A. R. Simpson, J. A. Liggett, and J. P. Vítkovský. 2002. “Leak detection in pipeline systems using the damping of fluid transients.” J. Hydraul. Eng. 128 (7): 697–711. https://doi.org/10.1061/(ASCE)0733-9429(2002)128%3A7(697).
Wu, Y., S. Liu, X. Wu, Y. Liu, and Y. Guan. 2016. “Burst detection in district metering areas using a data driven clustering algorithm.” Water Resour. 100 (Sep): 28–37. https://doi.org/10.1016/j.watres.2016.05.016.
Xu Q., J. Jiao, and S. Zhao. 2016. “Comparison and improvement of assessment indexes of water loss for distribution systems.” [In Chinese.] China Water Wastewater 32 (20): 14–18.
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.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 148 • Issue 3 • March 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Feb 4, 2021
Accepted: Nov 1, 2021
Published online: Dec 28, 2021
Published in print: Mar 1, 2022
Discussion open until: May 28, 2022
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
- Tingchao Yu, Xiaoyan Chen, Weimin Yan, Zhen Xu, Miaomiao Ye, Leak detection in water distribution systems by classifying vibration signals, Mechanical Systems and Signal Processing, 10.1016/j.ymssp.2022.109810, 185, (109810), (2023).
- Xuan Yang, Fuming Wang, Xiang Yu, Shaohui Li, Numerical and Experimental Study on Propagation Attenuation of Leakage Vibration Acceleration Signal of the Buried Water Pipe, Sustainability, 10.3390/su142316071, 14, 23, (16071), (2022).
- Xiaoqin Li, Xiaomei Wu, Mingzhuang Sun, Shengqiao Yang, Weikun Song, A Novel Intelligent Leakage Monitoring-Warning System for Sustainable Rural Drinking Water Supply, Sustainability, 10.3390/su14106079, 14, 10, (6079), (2022).
- Ganggui Guo, Shanshan Li, Yakun Liu, Ze Cao, Yangyu Deng, Prediction of Cavity Length Using an Interpretable Ensemble Learning Approach, International Journal of Environmental Research and Public Health, 10.3390/ijerph20010702, 20, 1, (702), (2022).
- Qingshuai Sun, Yingjie Zhang, Biliang Lu, Hualiang Liu, Flow Measurement-Based Self-Adaptive Line Segment Clustering Model for Leakage Detection in Water Distribution Networks, IEEE Transactions on Instrumentation and Measurement, 10.1109/TIM.2022.3165258, 71, (1-13), (2022).
- Xi Wan, Parisa Khorsandi Kuhanestani, Raziyeh Farmani, Edward Keedwell, Literature Review of Data Analytics for Leak Detection in Water Distribution Networks: A Focus on Pressure and Flow Smart Sensors, Journal of Water Resources Planning and Management, 10.1061/(ASCE)WR.1943-5452.0001597, 148, 10, (2022).
- Aliasghar Azma, Mohammad Tavakol Sadrabadi, Yakun Liu, Masoumeh Azma, Di Zhang, Ze Cao, Zhuoyue Li, Boosting ensembles for estimation of discharge coefficient and through flow discharge in broad-crested gabion weirs, Applied Water Science, 10.1007/s13201-022-01841-x, 13, 2, (2022).