Skip to main content
SpringerLink
Log in

Convolutional neural network for leak location in buried pipes of underground water supply

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

Water leakage in underground distribution networks is one of the greatest challenges faced by supply companies around the world. Moreover, current leakage detection and location methods are labor intensive or require a very experienced or highly qualified operator. Considering this, the goal of this manuscript is to apply a Machine Learning technique, more specifically a Convolutional Neural Network (CNN) model, to simplify the process of locating water leaks in underground pipelines, calculating the distance between the sensors and the epicenter of the leakage, from measurements on the ground surface. Machine Learning techniques have a great potential to identify the signature of a leak that might be hidden in the high background noise. In this work, accelerations were measured on the ground surface of an experimental platform, varying the vibration intensity of the underground source and the relative positioning of the sensors. The input matrices of the proposed CNN were formed by the Power Spectral Density of the collected signals and were used by three sensors concurrently in the measurements. After an extensive hyper-parameter search, four models that provided the best results were selected. The best model achieved a mean absolute error of 1.01 cm in the predicted leak distance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

Some data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Abadi M, Agarwal A, Barham P, et al. (2015) TensorFlow: large-scale machine learning on heterogeneous systems. Available at: www.tensorflow.org/ (accessed 20 september 2020)

  2. ABNT (Brazilian Association of Technical Standards). (2023) Laying of pipelines to convey raw and treated water, wastewater and urban drainage, using rigid, semirigid and flexible pipes (in Portuguese). ABNT NBR 17015, Rio de Janeiro, Brazil, pp. 114

  3. Almeida F, Brennan MJ, Joseph P, Whitfield S, Dray S, Paschoalini A (2014) On the acoustic filtering of the pipe and sensor in a buried plastic water pipe and its effect on leak detection: an experimental investigation. Sensors 14(3):5595–5610

    Article  Google Scholar 

  4. Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(2012):281–305

    MathSciNet  Google Scholar 

  5. Bianco MJ, Gerstoft P, Traer J, Ozanich E, Roch MA, Gannot S, Deledalle CA (2019) Machine learning in acoustics: theory and applications. J Acoust Soc Am 146(5):3590–3628

    Article  Google Scholar 

  6. Brennan MJ, Chapman DN, Joseph PF, Metje N, Muggleton JM, Rustighi E (2017) Achieving Zero Leakage by 2050 Leak Detection and Location Methods—Acoustic Leak Detection. UK Water Industry Research Limited, London

    Google Scholar 

  7. Brennan MJ, Karimib M, Muggleton JM et al (2018) On the effects of soil properties on leak noise propagation in plastic water distribution pipes. J Sound Vib 427(11):120–133

    Article  Google Scholar 

  8. Fang QS, Zhang JX, Xie CL, Yang YL (2019) Detection of multiple leakage points in water distribution networks based on convolutional neural networks. Water Supply 19(8):2231–2239

    Article  Google Scholar 

  9. Ferguson EL, Ramakrishnan R, Williams SB, and Jin CT (2017). Convolutional neural networks for passive monitoring of a shallow water environment using a single sensor. In: Proc: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA, 5–9 March 2017, pp. 2657–2661.

  10. Ferguson EL, Williams SB, Jin CT (2019) Convolutional neural network for single-sensor acoustic localization of a transiting broadband source in very shallow water. J Acoust Soc Am 146(6):4687–4698

    Article  Google Scholar 

  11. Frauendorfer R, Liemberger R (2010) The issues and challenges of reducing non-revenue water. Report Asian Development Bank, Philippines

    Google Scholar 

  12. Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: representing model uncertainty in deep learning. J Mach Learn Res 48:1050–1059

    Google Scholar 

  13. GO-Associates/Trata Brasil Institute. (2021). Water losses 2021 (SNIS 2019): challenges for water availability and advancing the efficiency of basic sanitation (in Portuguese). Report: Instituto Trata Brasil, São Paulo, Brazil.

  14. Goodfellow I, Bengio Y, Courville A (2016) Deep Learning – An MIT Press book. Available at: http://www.deeplearningbook.org. (accessed 14 jan. 2022)

  15. Hadadin NA, Qaqish M, Akawwi EJ, Bdour A (2010) Water shortage in jordan: sustainable solutions. Desalination 250(1):197–202

    Article  Google Scholar 

  16. Hamilton S, and Charalambous B (2013) Leak Detection: technology and implementation. Report, IWA Publishing, UK.

  17. Hyun SY, Jo YS, Oh HC, and Kirn S (2003) An experimental study on a ground-penetrating radar for detecting water-leaks in buried water transfer pipes. In: Proc: 6th international symposium on antennas, propagation and EM theory, Beijing, China, Oct 28–Nov 1 2003

  18. Hunaidi O, Wang A, Bracken M, Gambino T, and Fricke C (2004) Acoustic methods for locating leaks in municipal water pipe networks. In: Proc.: international conference on water demand management, Dead Sea, Jordan, May 30–june 3 2004

  19. Huang L, Li J, Hao H, Li X (2018) Micro-seismic event detection and location in underground mines by using convolutional neural networks (CNN) and deep learning. Tunn Undergr Space Technol 81(2018):265–276

    Article  Google Scholar 

  20. Kang J, Park Y, Lee J, Wang S, Eom D (2018) Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems. IEEE Trans Industr Electron 65(5):4279–4289

    Article  Google Scholar 

  21. Kovandzic M, Nikolic V, Al-Noori A, Ciric I, Simonovic M (2017) Near field acoustic localization under unfavorable conditions using feedforward neural network for processing time difference of arrival. Expert Syst Appl 71(2017):138–146

    Article  Google Scholar 

  22. Li R, Huang H, Xin K, Tao T (2014) A review of methods for burst/leakage detection and location in water distribution systems. Water Sci Technol Water Supply 15(3):429–441

    Article  Google Scholar 

  23. Liner CL (2012) Elements of seismic dispersion a somewhat practical guide to frequency-dependent phenomena. Society of Exploration Geophysicists (SEG), Tulsa

    Book  Google Scholar 

  24. Liu C, Li Y, Fang L, Han J, Xu M (2017) Leakage monitoring research and design for natural gas pipelines based on dynamic pressure waves. J Process Control 50(6):66–76

    Article  Google Scholar 

  25. Liu W, Yang Y, Xu M, Lu L, Liu Z, Shi Y (2020) Source localization in the deep ocean using a convolutional neural network. J Acoust Soc Am 147(4):L314–L319

    Article  Google Scholar 

  26. Loyola R, Bini LM (2015) Water shortage: a glimpse into the future. Braz J Nat Conserv 13(1):1–2

    Article  Google Scholar 

  27. Martini A, Troncossi M, Rivola A (2017) Vibroacoustic measurements for detecting water leaks in buried small-diameter plastic pipes. J Pipeline Syst Eng Pract 8(4):04017022

    Article  Google Scholar 

  28. Mehta L (2014) Water and human development. World Dev 59(2014):59–69

    Article  Google Scholar 

  29. Oppenheim AV, Schafer RW (1975) Digital signal processing. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  30. Pertila A, Brutti PSP, Omologo M (2018) Audio source separation and speech enhancement: multichannel source activity detection, localization, and tracking. Wiley Ltd, Hoboken

    Google Scholar 

  31. Proença MS, Paschoalini AT, Obata DHS (2023) Prediction of the probabilistic water leak location in underground pipelines using Monte Carlo simulation. Water Pract Technol 18(3):522–535

    Article  Google Scholar 

  32. Schwarz G (1978) Estimating the dimension of a model. J Acoust Soc Am 6(2):461–464

    MathSciNet  Google Scholar 

  33. Scussel O, Brennan MJ, Muggleton JM, Almeida FCL, and Paschoalini AT (2018). On the dynamic loading effects of soil on plastic water distribution pipes and its significance for leak detection using acoustics. In: Proc.: ASME International Mechanical Engineering Congress and Exposition, Pittsburg, USA, 9–15 November 2018,11: 1–7

  34. Scussel O, Brennan MJ, Almeida FCL, Muggleton JM, Rustighi E, Joseph PF (2021) Estimating the spectrum of leak noise in buried plastic water distribution pipes using acoustic or vibration measurements remote from the leak. Mech Syst Signal Process 147(15):107059–107071

    Article  Google Scholar 

  35. The Pandas development team (2022) Pandas-dev/pandas: Pandas (v1.5.1). Zenodo. Available at: doi.org/10.5281/zenodo.7 223478 (accessed 14 jan. 2022)

  36. Tsai YL, Chang HC, Lin SN, Chiou AH, Lee TL (2022) Using convolutional neural networks in the development of a water pipe leakage and location identification system. Appl Sci 12(16):8034. https://doi.org/10.3390/app12168034

    Article  Google Scholar 

  37. Tsutiya MT (2008) Water Supply: Management of Water and Electric Energy Losses in Supply Systems—Professional Training Guide: Level 2 (in Portuguese). Report for National Secretariat for Environmental Sanitation, Salvador, Brazil

  38. Shakmak B, and Al-Habaibeh A (2015) Detection of water leakage in buried pipes using infrared technology—A comparative study of using high and low resolution infrared cameras for evaluating distant remote detection. In: Proc.:2015 IEEE jordan conference on applied electrical engineering and computing technologies (AEECT), Amman, Jordan, 3–5 Nov 2015

  39. Shukla H, Piratla K (2020) Leakage detection in water pipelines using supervised classification of acceleration signals. Autom Constr 117(2020):103256

    Article  Google Scholar 

  40. SNIS—National Sanitation Information System (2020) Diagnosis of water and sewage services—2019 (in Portuguese). Report of National Secretariat for Environmental Sanitation, Brasília, Brazil

  41. Stone M (1974) Cross-Validatory Choice and Assessment of Statistical Predictions. J Royal Stat Soc Series B (Methodological) 36(2):111–147

    Article  MathSciNet  Google Scholar 

  42. UNESCO—United Nations Educational, Scientific and Cultural Organization (2020) WWDR 2020: Water and Climate Change. Report. UN World Water Development Report, Paris, France

  43. Van der Walt S, Colbert SC, Varoquaux G (2011) The numpy array: a structure for efficient numerical computation. Comput Sci Eng 13(2):22–30

    Article  Google Scholar 

  44. Wang J, Liu J, Liu H, Tian Z, Cheng F (2017) Modeling and locating underground water pipe leak with microseismic data. J Appl Geophys 136(1):1–8

    Article  Google Scholar 

  45. Wu Q, Lee CM (2019) A modified leakage localization method using multilayer perceptron neural networks in a pressurized gas pipe. Appl Sci 9(9):1954

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, São Paulo State University (UNESP), School of Engineering, Ilha Solteira, São Paulo, 15385-000, Brazil

    Otávio D. Z. Boaventura, Matheus S. Proença, Daniel H. S. Obata & Amarildo T. Paschoalini

Authors
  1. Otávio D. Z. Boaventura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matheus S. Proença
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel H. S. Obata
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amarildo T. Paschoalini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matheus S. Proença.

Additional information

Technical Editor: Aldemir Cavallini Jr.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Boaventura, O.D.Z., Proença, M.S., Obata, D.H.S. et al. Convolutional neural network for leak location in buried pipes of underground water supply. J Braz. Soc. Mech. Sci. Eng. 46, 352 (2024). https://doi.org/10.1007/s40430-024-04922-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40430-024-04922-x

Keywords

Search

Navigation