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Leakage zone identification for water distribution networks based on the alarm levels of pressure sensors

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Abstract

When a leakage event occurs, the values of pressure sensors in the water distribution network will drop, and leakage alarms will be triggered if the drop in pressure values exceed the alarm thresholds. Due to the similarity of the leakage characteristics between the adjacent nodes, it is difficult to identify the exact leakage node. Therefore, in this paper, a leakage zone identification method based on alarm levels and pattern identification is proposed. At first, leakage residual samples for each node are generated within the range of the leakage amount. To reduce the influence of nodes with similar leakage residual characteristics on the identification results, the residual values of each sample are converted into alarm levels. For the training samples, nodes with the same alarm level sample are merged into a node group and used as a label. Then, the Euclidean distance method is used to test the identification effect of the model. The enumeration method is adopted to optimize the sample interval, the alarm level interval and the feature dimension to enable the model to achieve an appropriate identification result. A life-sized network is presented in this paper to demonstrate the effectiveness of the proposed method. The results show that compared with the previous leakage zone identification method based on alarm characteristics, the proposed method can effectively reduce the size of the candidate leakage zone.

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References

  1. OECD (The Organisation for Economic Co-operation and Development) (2012) OECD environmental outlook to 2050: the consequences of inaction. OECD Publishing, Paris

    Google Scholar 

  2. China Urban Water Association (2015) Statistical yearbook of urban water supply. China Statistical Press, Beijing

    Google Scholar 

  3. Al Qahtani T, Yaakob MS, Yidris N et al (2020) A review on water leakage detection method in the water distribution network. J Adv Res Fluid Mech Therm Sci 68:152–163

    Article  Google Scholar 

  4. Xie X, Hou DB, Tang XY et al (2019) Leakage identification in water distribution network with error tolerance capability. Water Resour Manag 33(3):1233–1247

    Article  Google Scholar 

  5. Gao Y, Brennan MJ, Liu Y et al (2017) Improving the shape of the cross-correlation function for leak detection in a plastic water distribution pipe using acoustic signals. Appl Acoust 127(12):24–33

    Article  Google Scholar 

  6. Coster AD, Medina J, Nottebaere M et al (2019) Towards an improvement of GPR-based detection of pipes and leaks in water distribution networks. J Appl Geophys 162:138–151

    Article  Google Scholar 

  7. Li MH, Feng X (2022) Multisensor data fusion-based structural health monitoring for buriedmetallic pipelines under complicated stress states. J Civ Struct Health. https://doi.org/10.1007/s13349-022-00609-w

    Article  Google Scholar 

  8. Wu ZY, Sage P, Turtle D (2010) Pressure-dependent leak detection model and its application to a district water system. J Water Resour Plann Manage 136:116–128

    Article  Google Scholar 

  9. Geng ZQ, Hu X, Han YM et al (2019) A novel leakage-detection method based on sensitivity matrix of pipe flow: case study of water distribution systems. J Water Resour Plan Manag 145(2):04018094

    Article  Google Scholar 

  10. Sanz G, Pérez R, Kapelan Z et al (2015) Leak detection and localization through demand components calibration. J Water Resour Plan Manag 142(2):04015057

    Article  Google Scholar 

  11. Moasheri R, Ghazizadeh MJ, Tashayoei M (2021) Leakage detection in water networks by a calibration method. Flow Meas Instrum 80(24):101995

    Article  Google Scholar 

  12. Soldevila A, Fernandez-Canti RM, Blesa J et al (2017) Leak localization in water distribution networks using Bayesian classifiers. J Process Contr 55:1–9

    Article  CAS  Google Scholar 

  13. Kang J, Park YJ, Lee J et al (2018) Novel leakage detection by ensemble cnn-svm and graph-based localization in water distribution systems. IEEE Trans Ind Electron 65(5):4279–4289

    Article  Google Scholar 

  14. Guo GC, Yu X, Liu SM (2020) Leakage detection in water distribution systems based ontime–frequency convolutional neural network. J Water Resour Plan Manag 147(2):04020101

    Article  Google Scholar 

  15. Zhou X, Tang ZH, Xu WR et al (2019) Deep learning identifies accurate burst locations in water distribution networks. Water Res 166(12):115058

    Article  CAS  PubMed  Google Scholar 

  16. Qi ZX, Zheng FF, Guo DL et al (2018) Better understanding of the capacity of pressure sensor systems to detect pipe burst within water distribution networks. J Water Resour Plan Manag 144(7):04018035

    Article  Google Scholar 

  17. Rossman LA (2020) EPANET 2.2 online user’s manual. National Risk Management Research Laboratory, U.S. EPA, Cincinnati

    Google Scholar 

  18. Qi R, Li XP, Zhang Y (2020) Multi-classification algorithm for human motion recognition based on IR-UWB radar. IEEE Sens J 20(21):12848–12858

    Article  ADS  Google Scholar 

  19. Kapelan ZS, Savic DA, Walters GA (2015) Multiobjective design of water distribution systems under uncertainty. Water Resour Res 41(11):97–116

    Google Scholar 

  20. Pacchin E, Alvisi S, Franchini M (2017) Analysis of non-iterative methods and proposal of a new one for pressure-driven snapshot simulations with EPANET. Water Resour Manag 31(1):75–91

    Article  Google Scholar 

  21. Van Zyl JV, Cassa AM (2014) Modeling elastically deforming leaks in water distribution pipes. J Hydraul Eng 140(2):182–189

    Article  Google Scholar 

  22. Li JD, Cheng KW, Wang SH et al (2017) Feature selection: a data perspective. ACM Comput Surv 50(6):1–45

    Article  ADS  Google Scholar 

  23. Li X, Chu SP, Zhang TQ et al (2022) Leakage localization using pressure sensors and spatial clustering in water distribution systems. Water Supply 22(1):1020–1034

    Article  Google Scholar 

  24. Zhang QZ, Wu ZY, Zhao M 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

    Article  Google Scholar 

  25. Shao Y, Li X, Zhang TQ et al (2019) Time-series-based leakage detection using multiple pressure sensors in water distribution systems. Sensors 19(14):3070

    Article  PubMed  PubMed Central  ADS  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No.52079024), the Fundamental Research Funds for the Central Universities (Grant No. DUT20LAB133) and the National Key Research and Development Program of China (Grant No. 2016YFC0802402).

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Correspondence to Xin Feng.

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Chen, J., Feng, X. & Xiao, S. Leakage zone identification for water distribution networks based on the alarm levels of pressure sensors. J Civil Struct Health Monit 14, 15–27 (2024). https://doi.org/10.1007/s13349-022-00624-x

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