Advertisement

Leakage Identification in Water Distribution Networks with Error Tolerance Capability

  • Xiang Xie
  • Dibo HouEmail author
  • Xiaoyu Tang
  • Hongjian Zhang
Article
  • 76 Downloads

Abstract

Leakages in water distribution networks have caused considerable waste of water resources. Thus, this study proposes a novel method for hydraulically monitoring and identifying regions where leakages occur in near-real time. A large network is first divided into several identification regions. To exploit a strong constructive and discriminative power, sparse coding is used, thereby adaptively coding the information embedded in observed pressures efficiently and succinctly. And a linear classifier is trained to determine the most likely leakage regions. A benchmark case is presented in this study to demonstrate the effectiveness of the proposed method. Results indicate that the proposed method can identify leakage events with enhanced tolerance capability for measurement errors. The method is also partially effective for identifying two simultaneous leakages. Certain practical advice in balancing the number of sensors and regions is also discussed to enhance the application potential of this method.

Keywords

Water distribution network Leakage identification Pressure residual Sparse coding Dictionary learning 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China [grant number U1509208, 61573313]; the Key Technology Research and Development Program of Zhejiang Province [grant number 2015C03G2010034]; and the Fundamental Research Funds for the Central Universities [grant number 2017FZA5011].

References

  1. Aharon M, Elad M, Bruckstein A (2006) K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54 (11):4311–4322CrossRefGoogle Scholar
  2. Bapat RB (2014) Graphs and matrices. Springer, LondonGoogle Scholar
  3. Bentley Systems (2013) WaterGEMS V8i users manual. Watertown, CTGoogle Scholar
  4. Casillas MV, Garza-Castanon LE, Puig V, Vargas-Martinez A (2015) Leak signature space: an original representation for robust leak location in water distribution networks. Water 7(3):1129–1148CrossRefGoogle Scholar
  5. Colombo AF, Lee P, Karney BW (2009) A selective literature review of transient-based leak detection methods. J Hydro Environ Res 2(4):212–227CrossRefGoogle Scholar
  6. Duan H (2018) Uncertainty analysis of transient flow modeling and transient-based leak detection in elastic water pipeline systems. Water Resour Manag 29(14):5413–5427CrossRefGoogle Scholar
  7. Gertler JJ (1998) Fault detection and diagnosis in engineering systems. Marcel Dekker, New YorkGoogle Scholar
  8. Hajibandeh E, Nazif S (2018) Pressure zoning approach for leak detection in water distribution systems based on a multi objective ant colony optimization. Water Resour Manag 32(7):2287–2300CrossRefGoogle Scholar
  9. Jiang Z, Lin Z, Davis LS (2013) Label consistent k-SVD: learning a discriminative dictionary for recognition. IEEE Trans Pattern Anal Mach Intell 35(11):2651–2664CrossRefGoogle Scholar
  10. Laucelli D, Romano M, Savic D, Giustolisi O (2016) Detecting anomalies in water distribution networks using EPR modelling paradigm. J Hydroinf 18(3):409–427CrossRefGoogle Scholar
  11. Liu Z, Kleiner Y (2013) State of the art review of inspection technologies for condition assessment of water pipes. Measurement 46(1):1–15CrossRefGoogle Scholar
  12. Meseguer J, Mirats-Tur JM, Cembrano G, Puig V, Quevedo J, Rez R, Sanz G, Ibarra D (2014) A decision support system for on-line leakage localization. Environ Model Softw 60(10):331–345CrossRefGoogle Scholar
  13. Mounce S, Mounce R, Boxall J (2011) Novelty detection for time series data analysis in water distribution systems using support vector machines. J Hydroinf 13(4):672–686CrossRefGoogle Scholar
  14. Mounce SR, Boxall JB, Machell J (2010) Development and verification of an online artificial intelligence system for detection of bursts and other abnormal flows. J Water Resour Plan Manag 136(3):309–318CrossRefGoogle Scholar
  15. Olshausen BA, Field DJ (1997) Sparse coding with an overcomplete basis set: a strategy employed by V1? Vis Res 37(23):3311–3325CrossRefGoogle Scholar
  16. Piller O, Elhay S, Deuerlein J, Simpson AR (2017) Local sensitivity of pressure-driven modeling and demand-driven modeling steady-state solutions to variations in parameters. J Water Resour Plan Manag 143(2):04016,074CrossRefGoogle Scholar
  17. Romano M, Kapelan Z, Savic DA (2014) Automated detection of pipe bursts and other events in water distribution systems. J Water Resour Plan Manag 140 (4):457–467CrossRefGoogle Scholar
  18. Rosich A, Puig V, Casillas MV (2015) Leak localization in drinking water distribution networks using structured residuals. Int J Adapt Control Signal Process 29 (8):991–1007CrossRefGoogle Scholar
  19. Sivakumar P, Prasad RK (2014) Simulation of water distribution network under pressure-deficient condition. Water Resour Manag 28(10):3271–3290CrossRefGoogle Scholar
  20. Soldevila A, Blesa J, Tornil-Sin S, Duviella E, Fernandez-Canti RM, Puig V (2016) Leak localization in water distribution networks using a mixed model-based/data-driven approach. Control Eng Pract 55:162–173CrossRefGoogle Scholar
  21. Soldevila A, Fernandez-Canti RM, Blesa J, Tornil-Sin S, Puig V (2017) Leak localization in water distribution networks using bayesian classifiers. J Process Control 55:1–9CrossRefGoogle Scholar
  22. Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53(12):4655–4666CrossRefGoogle Scholar
  23. Walski TM (2003) Advanced water distribution modeling and management. Haetead Press, WaterburyGoogle Scholar
  24. Wu Y, Liu S, Xue W, Liu Y, Guan Y (2016) Burst detection in district metering areas using a data driven clustering algorithm. Water Res 100:28–37CrossRefGoogle Scholar
  25. Xie X, Zhou Q, Hou D, Zhang H (2018) Compressed sensing based optimal sensor placement for leak localization in water distribution networks. J Hydroinf 20(6):1286–1295CrossRefGoogle Scholar
  26. Ye G, Fenner RA (2011) Kalman filtering of hydraulic measurements for burst detection in water distribution systems. J Pipeline Syst Eng Pract 2(1):14–22CrossRefGoogle Scholar
  27. Zhang Q, Wu ZY, Zhao M, Qi J, Huang Y, Zhao H (2016) Leakage zone identification in large-scale water distribution systems using multiclass support vector machines. J Water Resour Plan Manag 142(11):04016,042CrossRefGoogle Scholar
  28. Zhang Z, Xu Y, Yang J, Li X, Zhang D (2015) A survey of sparse representation: algorithms and applications. IEEE Access 3:490–530CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Xiang Xie
    • 1
  • Dibo Hou
    • 1
    Email author
  • Xiaoyu Tang
    • 1
  • Hongjian Zhang
    • 1
  1. 1.State Key Laboratory of Industrial Control TechnologyZhejiang UniversityHangzhouChina

Personalised recommendations