Skip to main content

Advertisement

Log in

Data-driven approaches and model-based methods for detecting and locating leaks in water distribution systems: a literature review

  • Review
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Water distribution systems are made up of interconnected components that should allow water systems to meet demand, but leaks can waste enough water to limit supply. To limit financial losses, water utilities must quickly determine that a leak is occurring and where it is referred to as the localization of the leak. Over the years, there have been various methods proposed to detect and locate leaks. This literature review summarizes many of the methodologies introduced, categorizes them into data-driven approaches and model-based methods, and reviews their performance. Data-driven approaches demand efficient exploitation and use of available data from pressure and flow devices, and model-based methods require finely calibrated hydraulic models to reach a verdict. Data-driven approaches can manage uncertainty better than model-based methods.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

Data sharing does not apply to this article as this is a literature review.

Abbreviations

ACO:

Ant colony optimization

ANN:

Artificial neural network

AWWA:

American Water Works Association

BLIFF:

Burst location identification framework by fully linear DenseNet

BN:

Bayesian network

BPNN:

Back-propagation neural network

Cas-SVDD:

Cascade support vector data description

CNN:

Convolutional neural network

CtL-SSL:

Clustering-then-localization semi-supervised learning

CUSUM:

Cumulative sum

DenseNet:

Densely connected convolutional networks

DL:

Deep learning

DMA:

District metering areas

EM:

Expectation maximization

EWMA:

Exponential weighted moving average

ExSem:

Expert structural expectation–maximization

FLS:

Fuzzy logic system

FNR:

False-negative rate

FPR:

False-positive rate

IDW:

Inverse distance weighted

ISLMD:

Improved spline-local mean decomposition

IWA:

International water association

KPCA:

Kernel principal component analysis

LDA:

Linear discriminant analysis

LKF:

Linear Kalman filter

LMD:

Local mean decomposition

LP:

Local polynomial

LS:

Least squares

LSS:

Leak signature space

LSTM:

Long short-term memory

MDN:

Mixture density network

MLPNN:

Multilayer perceptron neural network

MDN:

Mixture density network

MNF:

Minimum night flow

NKF:

Nonlinear Kalman filter

NRW:

Non-revenue water

OC:

Ordinary cokriging

OK:

Ordinary kriging

PCA:

Principal component analysis

PKF:

Predictive Kalman filter

RMSE:

Root-mean-square error

RNN:

Recurrent neural network

SCADA:

Supervisory control and data acquisition

SIP:

Standardized innovation process

SOM:

Self-organizing maps

SPC:

Statistical process control

SVR:

Support vector machine

TDOA:

Time difference of arrival

TFR:

Transient frequency response

TNR:

True-negative rate

TPR:

True-positive rate

WDS:

Water distribution system

References

  1. Liemberger R, Marin P (2006) The challenge of reducing non-revenue water in developing countries—how the private sector can help: a look at performance-based service contracting

  2. Liemberger R, Wyatt A (2019) Quantifying the global non-revenue water problem. Water Supply 19(3):831–837

    Google Scholar 

  3. Xu J, Chai KTC, Wu G, Han B, Wai ELC, Li W, Gu Y (2018) Low-cost, tiny-sized MEMS hydrophone sensor for water pipeline leak detection. IEEE Trans Ind Electron 66(8):6374–6382

    Google Scholar 

  4. Britton TC, Stewart RA, O’Halloran KR (2013) Smart metering: enabler for rapid and effective post meter leakage identification and water loss management. J Clean Prod 54:166–176

    Google Scholar 

  5. Lee CW, Yoo DG (2021) Development of leakage detection model and its application for water distribution networks using RNN-LSTM. Sustainability 13(16):9262

    Google Scholar 

  6. Karim MR, Abbaszadegan M, LeChevallier M (2003) Potential for pathogen intrusion during pressure transients. J Am Water Works Ass 95(5):134–146

    Google Scholar 

  7. Fox S, Shepherd W, Collins R, Boxall J (2016) Experimental quantification of contaminant ingress into a buried leaking pipe during transient events. J Hydraul Eng 142(1):04015036

    Google Scholar 

  8. Wu Y, Liu S (2017) A review of data-driven approaches for burst detection in water distribution systems. Urban Water J 14(9):972–983

    Google Scholar 

  9. Wang XJ, Simpson AR, Lambert MF, Vítkovský JP (2001) Leak detection in pipeline systems using hydraulic methods: a review. In: Conference on hydraulics in civil engineering, the institution of engineers, Australia, Hobart (pp. 23–30)

  10. Lambert A (1994) Accounting for losses: the bursts and background concept. Water Environ J 8(2):205–214

    Google Scholar 

  11. Farah E, Shahrour I (2017) Leakage detection using smart water system: combination of water balance and automated minimum night flow. Water Resour Manage 31(15):4821–4833

    Google Scholar 

  12. Mounce SR, Mounce RB, Jackson T, Austin J, Boxall JB (2014) Pattern matching and associative artificial neural networks for water distribution system time series data analysis. J Hydroinf 16(3):617–632

    Google Scholar 

  13. Loveday M, Dixon J (2005) DMA sustainability in developing countries. In: Proceedings. IWA Specialized Conference: Leakage

  14. Mutikanga HE, Sharma SK, Vairavamoorthy K (2013) Methods and tools for managing losses in water distribution systems. J Water Resour Plan Manag 139(2):166–174

    Google Scholar 

  15. Zaman D, Tiwari MK, Gupta AK, Sen D (2020) A review of leakage detection strategies for pressurized pipeline in steady-state. Eng Fail Anal 109:104264

    Google Scholar 

  16. Xie J, Xu X, Dubljevic S (2019) Long range pipeline leak detection and localization using discrete observer and support vector machine. AIChE J 65(7):e16532

    Google Scholar 

  17. Xue Z, Tao L, Fuchun J, Riehle E, Xiang H, Bowen N, Singh RP (2020) Application of acoustic intelligent leak detection in an urban water supply pipe network. J Water Supply Res Technol AQUA 69(5):512–520

    Google Scholar 

  18. Li R, Huang H, Xin K, Tao T (2015) A review of methods for burst/leakage detection and location in water distribution systems. Water Sci Technol Water Supply 15(3):429–441

    Google Scholar 

  19. Colombo AF, Lee P, Karney BW (2009) A selective literature review of transient-based leak detection methods. J Hydro Environ Res 2(4):212–227

    Google Scholar 

  20. Hu Z, Chen B, Chen W, Tan D, Shen D (2021) Review of model-based and data-driven approaches for leak detection and location in water distribution systems. Water Supply 21(7):3282–3306

    Google Scholar 

  21. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group* (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 151(4): 264-269

  22. Mounce SR, Day AJ, Wood AS, Khan A, Widdop PD, Machell J (2002) A neural network approach to burst detection. Water Sci Technol 45(4–5):237–246

    Google Scholar 

  23. Mounce SR, Machell J (2006) Burst detection using hydraulic data from water distribution systems with artificial neural networks. Urban Water Journal 3(1):21–31

    Google Scholar 

  24. Mounce SR, Boxall JB, Machell J (2007) An artificial neural network/fuzzy logic system for DMA flow meter data analysis providing burst identification and size estimation. Water management challenges in global change, pp 313–320

  25. Aksela K, Aksela M, Vahala R (2009) Leakage detection in a real distribution network using a SOM. Urban Water J 6(4):279–289

    Google Scholar 

  26. 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):309–318

    Google Scholar 

  27. Mounce SR, Mounce RB, Boxall JB (2011) Novelty detection for time series data analysis in water distribution systems using support vector machines. J Hydroinf 13(4):672–686

    Google Scholar 

  28. Nasir MT, Mysorewala M, Cheded L, Siddiqui B, Sabih M (2014) Measurement error sensitivity analysis for detecting and locating leak in pipeline using ANN and SVM. In: 2014 IEEE 11th international multi-conference on systems, signals and devices (SSD14), IEEE, (pp. 1–4)

  29. Romano M, Kapelan Z, Savić DA (2013) Geostatistical techniques for approximate location of pipe burst events in water distribution systems. J Hydroinf 15(3):634–651

    Google Scholar 

  30. Ye G, Fenner RA (2011) Kalman filtering of hydraulic measurements for burst detection in water distribution systems. J Pipeline Syst Eng Pract 2(1):14–22

    Google Scholar 

  31. Palau Estevan CV, Arregui de la Cruz F, Carlos Alberola MDM (2012) Burst detection in water networks using principal component analysis. J Water Resour Plan Manag 138(1):47–54

    Google Scholar 

  32. Eliades DG, Polycarpou MM (2012) Leakage fault detection in district metered areas of water distribution systems. J Hydroinf 14(4):992–1005

    Google Scholar 

  33. Ye G, Fenner RA (2014) Weighted least squares with expectation-maximization algorithm for burst detection in UK water distribution systems. J Water Resour Plan Manag 140(4):417–424

    Google Scholar 

  34. Bakker M, Vreeburg JHG, Van De Roer M, Rietveld LC (2014) Heuristic burst detection method using flow and pressure measurements. J Hydroinf 16(5):1194–1209

    Google Scholar 

  35. Hutton C, Kapelan Z (2015) Real-time burst detection in water distribution systems using a Bayesian demand forecasting methodology. Procedia Eng 119:13–18

    Google Scholar 

  36. Jung D, Lansey K (2015) Water distribution system burst detection using a nonlinear Kalman filter. J Water Resour Plan Manag 141(5):04014070

    Google Scholar 

  37. Loureiro D, Amado C, Martins A, Vitorino D, Mamade A, Coelho ST (2016) Water distribution systems flow monitoring and anomalous event detection: a practical approach. Urban Water J 13(3):242–252

    Google Scholar 

  38. Karray F, Garcia-Ortiz A, Jmal MW, Obeid AM, Abid M (2016) Earnpipe: a testbed for smart water pipeline monitoring using wireless sensor network. Procedia Comput Sci 96:285–294

    Google Scholar 

  39. Laucelli D, Romano M, Savić D, Giustolisi O (2016) Detecting anomalies in water distribution networks using EPR modelling paradigm. J Hydroinf 18(3):409–427

    Google Scholar 

  40. Leu SS, Bui QN (2016) Leak prediction model for water distribution networks created using a Bayesian network learning approach. Water Resour Manage 30(8):2719–2733

    Google Scholar 

  41. Jia Z, Ren L, Li H, Sun W (2018) Pipeline leak localization based on FBG hoop strain sensors combined with BP neural network. Appl Sci 8(2):146

    Google Scholar 

  42. Wu ZY, Sage P, Turtle D (2010) Pressure-dependent leak detection model and its application to a district water system. J Water Resour Plan Manag 136(1):116–128

    Google Scholar 

  43. Huang P, Zhu N, Hou D, Chen J, Xiao Y, Yu J, Zhang H (2018) Real-time burst detection in district metering areas in water distribution system based on patterns of water demand with supervised learning. Water 10(12):1765

    Google Scholar 

  44. Gómez-Camperos JA, Espinel-Blanco EE, Regino-Ubarnes FJ (2019) Diagnosis of horizontal pipe leaks using neural networks. In: Journal of physics: conference series (Vol. 1388, No. 1, p. 012032). IOP Publishing

  45. Sun C, Parellada B, Puig V, Cembrano G (2019) Leak localization in water distribution networks using pressure and data-driven classifier approach. Water 12(1):54

    Google Scholar 

  46. Rayaroth R, S G (2019) Random bagging classifier and shuffled frog leaping based optimal sensor placement for leakage detection in WDS. Water Resour Manage 33(9):3111–3125

    Google Scholar 

  47. Zhou M, Zhang Q, Liu Y, Sun X, Cai Y, Pan H (2019) An integration method using kernel principal component analysis and cascade support vector data description for pipeline leak detection with multiple operating modes. Processes 7(10):648

    Google Scholar 

  48. Soldevila A, Blesa J, Fernandez-Canti RM, Tornil-Sin S, Puig V (2019) Data-driven approach for leak localization in water distribution networks using pressure sensors and spatial interpolation. Water 11(7):1500

    Google Scholar 

  49. Weirong X, Zhou X, Xin K, Boxall J, Yan H, Tao T (2020) Disturbance extraction for burst detection in water distribution networks using pressure measurements. Water Resour Res. https://doi.org/10.1029/2019WR025526

    Article  Google Scholar 

  50. Wu Y, Liu S, Wu X, Liu Y, Guan Y (2016) Burst detection in district metering areas using a data driven clustering algorithm. Water Res 100:28–37

    Google Scholar 

  51. Wu Y, Liu S, Smith K, Wang X (2018) Using correlation between data from multiple monitoring sensors to detect bursts in water distribution systems. J Water Resour Plan Manag 144(2):04017084

    Google Scholar 

  52. Geelen CV, Yntema DR, Molenaar J, Keesman KJ (2019) Monitoring support for water distribution systems based on pressure sensor data. Water Resour Manage 33(10):3339–3353

    Google Scholar 

  53. Xing L, Sela L (2019) Unsteady pressure patterns discovery from high-frequency sensing in water distribution systems. Water Res 158:291–300

    Google Scholar 

  54. Quiñones-Grueiro M, Verde C, Llanes-Santiago O (2019) Novel leak location approach in water distribution networks with zone clustering and classification. In: Carrasco-Ochoa JA, Martínez-Trinidad JF, Olvera-López JA, Salas J (eds) Pattern recognition: 11th Mexican conference, MCPR 2019, Querétaro, Mexico, June 26–29, 2019, proceedings. Springer International Publishing, Cham, pp 37–46. https://doi.org/10.1007/978-3-030-21077-9_4

    Chapter  Google Scholar 

  55. Fan X, Yu X (2021) An innovative machine learning based framework for water distribution network leakage detection and localization. Struct Health Monit 21(4):1626–1644

    Google Scholar 

  56. Yuan Q, Shen H, Li T, Li Z, Li S, Jiang Y, Zhang L (2020) Deep learning in environmental remote sensing: achievements and challenges. Remote Sens Environ 241:111716

    Google Scholar 

  57. Quiñones-Grueiro M, Milián MA, Rivero MS, Neto AJS, Llanes-Santiago O (2021) Robust leak localization in water distribution networks using computational intelligence. Neurocomputing 438:195–208

    Google Scholar 

  58. Wang X, Guo G, Liu S, Wu Y, Xu X, Smith K (2020) Burst detection in district metering areas using deep learning method. J Water Resour Plan Manag 146(6):04020031

    Google Scholar 

  59. Kang J, Park YJ, Lee J, Wang SH, Eom DS (2017) Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems. IEEE Trans Industr Electron 65(5):4279–4289

    Google Scholar 

  60. Zhou X, Tang Z, Xu W, Meng F, Chu X, Xin K, Fu G (2019) Deep learning identifies accurate burst locations in water distribution networks. Water Res 166:115058

    Google Scholar 

  61. Zhang J, Lu C, Li X, Kim HJ, Wang J (2019) A full convolutional network based on DenseNet for remote sensing scene classification. Math Biosci Eng 16(5):3345–3367

    Google Scholar 

  62. Zhou M, Pan Z, Liu Y, Zhang Q, Cai Y, Pan H (2019) Leak detection and location based on ISLMD and CNN in a pipeline. IEEE Access 7:30457–30464

    Google Scholar 

  63. Cody RA, Tolson BA, Orchard J (2020) Detecting leaks in water distribution pipes using a deep autoencoder and hydroacoustic spectrograms. J Comput Civ Eng 34(2):04020001

    Google Scholar 

  64. Liao Z, Yan H, Tang Z, Chu X, Tao T (2021) Deep learning identifies leak in water pipeline system using transient frequency response. Process Saf Environ Prot 155:355–365

    Google Scholar 

  65. Pudar RS, Liggett JA (1992) Leaks in pipe networks. J Hydraul Eng 118(7):1031–1046

    Google Scholar 

  66. Pérez R, Puig V, Pascual J, Peralta A, Landeros E, Jordanas L (2009) Pressure sensor distribution for leak detection in Barcelona water distribution network. Water Sci Technol Water Supply 9(6):715–721

    Google Scholar 

  67. Pérez R, Puig V, Pascual J, Quevedo J, Landeros E, Peralta A (2011) Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks. Control Eng Pract 19(10):1157–1167

    Google Scholar 

  68. Perez R, Sanz G, Puig V, Quevedo J, Escofet MAC, Nejjari F, Sarrate R (2014a) Leak localization in water networks: a model-based methodology using pressure sensors applied to a real network in Barcelona [applications of control]. IEEE Control Syst Mag 34(4):24–36

    MATH  Google Scholar 

  69. Pérez R, Cugueró MA, Cugueró J, Sanz G (2014b) Accuracy assessment of leak localisation method depending on available measurements. Procedia Eng 70:1304–1313

    Google Scholar 

  70. Casillas MV, Garza-Castañón LE, Puig V, Vargas-Martinez A (2015) Leak signature space: an original representation for robust leak location in water distribution networks. Water 7(3):1129–1148

    Google Scholar 

  71. Salguero FJ, Cobacho R, Pardo MA (2019) Unreported leaks location using pressure and flow sensitivity in water distribution networks. Water Supply 19(1):11–18

    Google Scholar 

  72. Geng Z, Hu X, Han Y, Zhong Y (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

    Google Scholar 

  73. Jiménez-Cabas J, Romero-Fandiño E, Torres L, Sanjuan M, López-Estrada FR (2018) Localization of leaks in water distribution networks using flow readings. IFAC-PapersOnLine 51(24):922–928

    Google Scholar 

  74. 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 Manage 32(7):2287–2300

    Google Scholar 

  75. Nasirian A, Maghrebi MF, Yazdani S (2013) Leakage detection in water distribution network based on a new heuristic genetic algorithm model. J Water Resour Prot 05(03):294–303

    Google Scholar 

  76. Sanz G, Pérez R, Kapelan Z, Savic D (2016) Leak detection and localization through demand components calibration. J Water Resour Plan Manag 142(2):04015057

    Google Scholar 

  77. Goulet JA, Coutu S, Smith IF (2013) Model falsification diagnosis and sensor placement for leak detection in pressurized pipe networks. Adv Eng Inform 27(2):261–269

    Google Scholar 

  78. Moser G, Paal SG, Smith IF (2015) Performance comparison of reduced models for leak detection in water distribution networks. Adv Eng Inform 29(3):714–726

    Google Scholar 

  79. Moser G, Paal SG, Jlelaty D, Smith IF (2016) An electrical network for evaluating monitoring strategies intended for hydraulic pressurized networks. Adv Eng Inform 30(4):672–686

    Google Scholar 

  80. Jensen HA, Jerez DJ (2019) A Bayesian model updating approach for detection-related problems in water distribution networks. Reliab Eng Syst Saf 185:100–112

    Google Scholar 

  81. Moser G, Paal SG, Smith IF (2018) Leak detection of water supply networks using error-domain model falsification. J Comput Civ Eng 32(2):04017077

    Google Scholar 

  82. Shao Y, Li X, Zhang T, Chu S, Liu X (2019) Time-series-based leakage detection using multiple pressure sensors in water distribution systems. Sensors 19(14):3070

    Google Scholar 

  83. Deng X, Liu Q, Deng Y, Mahadevan S (2016) An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Inf Sci 340:250–261

    Google Scholar 

  84. Romano M, Kapelan Z, Savić DA (2014) Automated detection of pipe bursts and other events in water distribution systems. J Water Resour Plan Manag 140(4):457–467

    Google Scholar 

  85. Romano M, Kapelan Z, Savić DA (2014) Evolutionary algorithm and expectation maximization strategies for improved detection of pipe bursts and other events in water distribution systems. J Water Resour Plan Manag 140(5):572–584

    Google Scholar 

  86. Srirangarajan S, Allen M, Preis A, Iqbal M, Lim HB, Whittle AJ (2013) Wavelet-based burst event detection and localization in water distribution systems. J Sign Process Syst 72(1):1–16

    Google Scholar 

  87. Tao T, Huang H, Li F, Xin K (2014) Burst detection using an artificial immune network in water-distribution systems. J Water Resour Plan Manag 140(10):04014027

    Google Scholar 

  88. Zan TTT, Lim HB, Wong KJ, Whittle AJ, Lee BS (2014) Event detection and localization in urban water distribution network. IEEE Sens J 14(12):4134–4142

    Google Scholar 

  89. 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):04016042

    Google Scholar 

  90. 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 55:162–173

    Google Scholar 

  91. 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 55:1–9

    Google Scholar 

  92. Bakker M, Trietsch EA, Vreeburg JHG, Rietveld LC (2014b) Analysis of historic bursts and burst detection in water supply areas of different size. Water Sci Technol Water Supply 14(6):1035–1044

    Google Scholar 

Download references

Acknowledgements

This project was supported by the Smart Cities Innovation Partnership Program (Award Number 134146) of the New York State Empire State Development (ESD).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yong Wang.

Ethics declarations

Conflict of interest

There is no conflict of interest associated with this paper.

Additional information

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

Nimri, W., Wang, Y., Zhang, Z. et al. Data-driven approaches and model-based methods for detecting and locating leaks in water distribution systems: a literature review. Neural Comput & Applic 35, 11611–11623 (2023). https://doi.org/10.1007/s00521-023-08497-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08497-x

Keywords

Navigation