Abstract
With the development of cities, the water resources loss and environmental pollution caused by pipeline leakage need to be solved urgently. In this paper, a probabilistic method of model-based Bayesian analysis is designed to solve the multi-leakage detection problem of reservoir pipeline valve system. Bayesian inference selects the model best suited to the measured data. This process estimates the number of leaks and then extracts the leak locations from a model that measures data preferences. In this paper, according to the characteristics of water head in pipeline, the Likelihood function of water head for Bayesian evidence calculation is given. It solves the problem that the location ability of recent research methods is limited by leakage location. The number and locations of leakages can be determined simultaneously. Different experimental Settings and scenarios are given to verify the effectiveness of the proposed method. For three leaks that do not contain tight leaks, the RMSE of each leak is 2.3068 m, and in the case of tight leaks, the average RMSE of each leak is 3.5011 m. The results demonstrating that this model-based Bayesian analysis is an accurate tool for leakage enumeration and location estimation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
All authors make sure that all data and materials as well as software application or custom code support the published claims and comply with field standards.
References
Beaton D, Xiang N (2017) Room acoustic modal analysis using Bayesian inference. J Acoust Soc Am 141(6):4480–4493
Bush D, Xiang N (2018) A model-based Bayesian framework for sound source enumeration and direction of arrival estimation using a coprime microphone array. J Acoust Soc Am 143(6):3934–3945
Chaudhry MH (2014) Applied hydraulic transients, 3rd edn. Springer, New York
Colombo AF, Karney BW (2002) Energy and costs of leaky pipes: toward a comprehensive picture. J Water Resour Plann Manag ASCE 128(6):441–450
Covas D, Ramos H, Graham N, Maksimovic C (2004) Application of hydraulic transients for leak detection in water supply systems. Water Sci Technol IWA 4(5–6):365–374
Del Teso R, Gómez E, Estruch-Juan E, Cabrera E (2019) Topographic energy management in water distribution systems. Water Resour Manag EWRA 33(12):4385–4400
Duan HF (2016) Transient frequency response based leak detection in water supply pipeline systems with branched and looped junctions. J Hydroinform IWA 19(1):17–30
Duan HF (2018) Accuracy and sensitivity evaluation of TFR method for leak detection in multiple-pipeline water supply systems. Water Resour Manag EWRA 32(6):2147–2164
Duan HF, Che TC, Lee PJ, Ghidaoui MS (2018) Influence of nonlinear turbulent friction on the system frequency response in transient pipe flow modelling and analysis. J Hydraul Res IAHR 56(4):451–463
Escolano J, Xiang N, Perez-Lorenzo JM, Cobos M, Lopez JJ (2014) A Bayesian direction-of-arrival model for an undetermined number of sources using a two-microphone array. J Acoust Soc Am 135(2):742–753
Gupta A, DA Kulat K (2018) Selective literature review on leak management techniques for water distribution system. Water Resour Manag 32(10):3247–3269
Kim S (2016) Impedance method for abnormality detection of a branched pipeline system. Water Resour Manag EWRA 30(3):1101–1115
Knuth KH, Habeck M, Malakare NK, Mubeen AM, Placek B (2015) Bayesian evidence and model selection. Digital Signal Process 47:50–67
Landschoot CR, Xiang N (2019) Model-based Bayesian direction of arrival analysis for sound sources using a spherical microphone array. J Acoust Soc Am 146(6):4936–4946
Li J, Wu Y, Changgang L (2020) Pipeline leak detection using the multiple signal classification-like method. J Hydroinform IWA 22(5):1321–1337
Li J, Zheng Q, Zhihong Q, Xiaoping Y (2019) A novel location algorithm for pipeline leakage based on the attenuation of negative pressure wave. Process Saf Environ Prot 123:309–316
Naik H, Nagarajappa DP (2017) Rural wastewater treatability studies by soil aquifer treatment in conjunction with Magnifera indica
Pasalwad S, Nanekar C, Gaikwad A (2019) Feasibility study of cow dung ash as a disinfectant in water. Glob Res Dev J Eng 7(4):28–35
Sattar AM, Chaudhry MH (2008) Leak detection in pipelines by frequency response method. J Hydraul Res IAHR 46(EI1):138–151
Sivia D, Skilling J (2006) Data analysis: a Bayesian tutorial. Clarendon Press, Oxford University Press
Skilling J (2004) Nested Sampling AIP Conference Proceedings 735:395–405
Skilling J (2006) Nested sampling for general Bayesian computation. Bayesian Anal 1(4):833–859
Soares AK, Covas DIC, Reis LFR (2011) Leak detection by inverse transient analysis in an experimental PVC pipe system. J Hydroinform IWA 13(2):153–166
Stephens ML, Lambert MF, Simpson AR (2013) Determining the internal wall condition of a water pipeline in the field using an inverse transient. J Hydraul Eng ASCE 139(3):310–324
Sun JL, Wang RH, Duan HF (2016) Multiple-fault detection in water pipelines using transient-based time-frequency analysis. J Hydroinform IWA 18(6):975–989
Suseela K, Devika BM, Prasad BDV (2020) Potential waste water reuse. Glob Res Dev J Eng 1(6):20–28
Vitkovsky JP, Lambert MF, Simpson AR, Liggett JA (2007) Experimental observation and analysis of inverse transients for pipeline leak detection. J Water Resour Plann Manag ASCE 133(6):519–530
Wang XJ, Lambert MF, Simpson AR, Liggett JA, Vitkovsky JP (2002) Leak detection in pipelines using the damping of fluid transients. J Hydraul Eng ASCE 128(7):697–711
Wang X, Ghidaoui MS (2018) Pipeline leak detection using the matched-field processing method. J Hydraul Eng ASCE 144(6):04018030
Wang X, Palomar DP, Licheng Z, Ghidaoui MS, Murch RD (2019) Spectral-based methods for pipeline leakage localization. J Hydraul Eng ASCE 145(3):04018089
Acknowledgements
This paper is supported by the key Science Foundation of the Department of Science and Technology of Jilin Province (Grant Nos. 20180201081SF, 20190303082SF), science and technology project of The Education Department of Jilin Province (Grant No. JJKH20200983KJ), and the Fund project of The Science and Technology Department of Jilin Province (Grant No. 20200201046JC). Thanks for the permission to publish this paper.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by JL, YW and CL. The first draft of the manuscript was written by YW and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
All Authors declare that they have no competing interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Li, J., Wu, Y., Zheng, W. et al. A Model-Based Bayesian Framework for Pipeline Leakage Enumeration and Location Estimation. Water Resour Manage 35, 4381–4397 (2021). https://doi.org/10.1007/s11269-021-02955-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-021-02955-8