Leak Detection Methods in Water Distribution Networks: A Comparative Survey on Artificial Intelligence Applications
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 13, Issue 3
Abstract
Essentially, water is an extremely vital resource for human beings. However, each year, a significant amount of water is lost because of leakages in multiple water distribution systems. From this perspective, much ink has been spilled upon the issue of water leakage detection and location. Indeed, since the emergence of data and interest in the development of artificial intelligence (AI) techniques, leak detection and location solutions have been optimized. This survey aims to present a comprehensive review of leak detection and location techniques in water distribution networks (WDNs). The different categories of leak detection and location solutions are set forward, in particular the intelligent ones. A comparative study between AI algorithms is performed using scenarios from the LeakDB data set. To our knowledge, this is the first work that uses a common benchmark data set to offer a comparative experimental study of the most used algorithms in leak detection. The selective choices of scenarios and experiments grant a deep understanding of the leak detection works, as well as a support for future research to develop artificial intelligence methods in this area.
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Data Availability Statement
The pressure and flow data sets generated and used during the study are available at Vrachimis and Kyriakou (2018); it is an open-access benchmark data set. However, models and data analysis and detection codes used during the study are confidential and were provided by the financiers as indicated in the Acknowledgments, and may only be provided with their agreement.
Acknowledgments
This research work was accomplished in collaboration between Sofia Technologies Company and CES Lab in the National Engineering School of Sfax. This project is carried out under the MOBIDOC scheme, funded by the EU through the EMORI program and managed by the ANPR.
References
Aamodt, A., and E. Plaza. 1994. “Case-based reasoning: Foundational issues, methodological variations, and system approaches.” AI Commun. 7 (1): 39–59. https://doi.org/10.3233/AIC-1994-7104.
Abdelhafidh, M., L. C. Fourati, M. Fourati, and A. Abidi. 2017. “Hybrid mechanism for remote water pipeline monitoring system.” In Proc., 2017 13th Int. Wireless Communications and Mobile Computing Conf. (IWCMC), 2140–2145. New York: IEEE.
Adedeji, K., Y. Hamam, B. Abe, and A. M. Abu-Mahfouz. 2017a. “Leakage detection algorithm integrating water distribution networks hydraulic model.” In Proc., Choosing Right Model Appl. Hydraulics (SimHydro), 1–9. Berlin: Springer.
Adedeji, K. B., Y. Hamam, B. T. Abe, and A. M. Abu-Mahfouz. 2017b. “Towards achieving a reliable leakage detection and localization algorithm for application in water piping networks: An overview.” IEEE Access 5: 20272–20285. https://doi.org/10.1109/ACCESS.2017.2752802.
Adegboye, M. A., W.-K. Fung, and A. Karnik. 2019. “Recent advances in pipeline monitoring and oil leakage detection technologies: Principles and approaches.” Sensors 19 (11): 2548. https://doi.org/10.3390/s19112548.
Alves Coelho, J., A. Glória, and P. Sebastião. 2020. “Precise water leak detection using machine learning and real-time sensor data.” IoT 1 (2): 474–493. https://doi.org/10.3390/iot1020026.
Bakhtawar, B., and T. Zayed. 2021. “Review of water leak detection and localization methods through hydrophone technology.” J. Pipeline Syst. Eng. Pract. 12 (4): 03121002. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000574.
Bakker, M., J. Vreeburg, K. Van Schagen, and L. Rietveld. 2013. “A fully adaptive forecasting model for short-term drinking water demand.” Environ. Modell. Software 48 (Oct): 141–151. https://doi.org/10.1016/j.envsoft.2013.06.012.
Basseville, M., and I. V. Nikiforov. 1993. Vol. 104 of Detection of abrupt changes: Theory and application. Englewood Cliffs, NJ: Prentice Hall.
Bicik, J., Z. Kapelan, C. Makropoulos, and D. A. Savić. 2011. “Pipe burst diagnostics using evidence theory.” J. Hydroinf. 13 (4): 596–608. https://doi.org/10.2166/hydro.2010.201.
bin Md Akib, A., N. bin Saad, and V. Asirvadam. 2011. “Pressure point analysis for early detection system.” In Proc., 2011 IEEE 7th Int. Colloquium on Signal Processing and its Applications, 103–107. New York: IEEE.
Bjerke, M. 2019. “Leak detection in water distribution networks using gated recurrent neural networks.” M.S. thesis, Dept. of Computer Technology and Informatics, Norwegian Univ. of Science and Technology.
Blázquez-García, A., A. Conde, U. Mori, and J. A. Lozano. 2021. “Water leak detection using self-supervised time series classification.” Inf. Sci. 574 (Oct): 528–541. https://doi.org/10.1016/j.ins.2021.06.015.
Bohorquez, J., B. Alexander, A. R. Simpson, and M. F. Lambert. 2020. “Leak detection and topology identification in pipelines using fluid transients and artificial neural networks.” J. Water Resour. Plann. Manage. 146 (6): 04020040. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001187.
Brdys, M. A., and B. Ulanicki. 1996. “Operational control of water systems: Structures, algorithms and applications.” Automatica 32 (11): 1619–1620. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001187.
Brouwer, R. K. 2002. “A feed-forward network for input that is both categorical and quantitative.” Neural Netw. 15 (7): 881–890. https://doi.org/10.1016/S0893-6080(02)00090-4.
Caputo, A. C., and P. M. Pelagagge. 2002. “An inverse approach for piping networks monitoring.” J. Loss Prev. Process Ind. 15 (6): 497–505. https://doi.org/10.1016/S0950-4230(02)00036-0.
Chan, T. K., C. S. Chin, and X. Zhong. 2018. “Review of current technologies and proposed intelligent methodologies for water distributed network leakage detection.” IEEE Access 6: 78846–78867. https://doi.org/10.1109/ACCESS.2018.2885444.
Chen, J., P. Tang, T. Rakstad, M. Patrick, and X. Zhou. 2020. “Augmenting a deep-learning algorithm with canal inspection knowledge for reliable water leak detection from multispectral satellite images.” Adv. Eng. Inf. 46 (Oct): 101161. https://doi.org/10.1016/j.aei.2020.101161.
Chuang, W.-Y., Y.-L. Tsai, and L.-H. Wang. 2019. “Leak detection in water distribution pipes based on CNN with MEL frequency cepstral coefficients.” In Proc., 2019 3rd Int. Conf. on Innovation in Artificial Intelligence, 83–86. New York: Association for Company Machinery.
Cody, R., S. Narasimhan, and B. Tolson. 2017. “One-class SVM–Leak detection in water distribution systems.” In Proc., Computing and Control for the Water Industry, CCWI. Hague, Netherlands: Computing and Control for the Water Industry Sheffield.
Cody, R. A., B. A. Tolson, and J. Orchard. 2020. “Detecting leaks in water distribution pipes using a deep autoencoder and hydroacoustic spectrograms.” J. Comput. Civ. Eng. 34 (2): 04020001. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000881.
Covas, D., and H. Ramos. 2010. “Case studies of leak detection and location in water pipe systems by inverse transient analysis.” J. Water Resour. Plann. Manage. 136 (2): 248–257. https://doi.org/10.1061/(ASCE)0733-9496(2010)136:2(248).
Demirci, S., E. Yigit, I. H. Eskidemir, and C. Ozdemir. 2012. “Ground penetrating radar imaging of water leaks from buried pipes based on back-projection method.” NDT&E Int. 47 (Apr): 35–42. https://doi.org/10.1016/j.ndteint.2011.12.008.
Fereidooni, Z., H. Tahayori, and A. Bahadori-Jahromi. 2021. “A hybrid model-based method for leak detection in large scale water distribution networks.” J. Ambient Intell. Hum. Comput. 12 (2): 1613–1629. https://doi.org/10.1007/s12652-020-02233-2.
Ferreira, P. P. D., D. P. Kappes, E. M. Oliveira, M. L. da Fonseca, and A. P. S. Medeiros. 2018. “Leak detection system using machine learning techniques.” In Proc., 6th Int. Congress on Automation in Mining. State of Minas Gerais, Brazil: Brazilian Association of Metallurgy.
Geiger, G., D. Vogt, and R. Tetzner. 2006. “State-of-the-art in leak detection and localization.” Oil Gas Eur. Mag. 32 (4): 193.
Geiger, I. G. 2005. “Principles of leak detection.” In Fundamentals of leak detection. KROHNE oil and gas, 399. Duisburg, Germany: KROHNE Messtechnik GmbH.
Giustolisi, O., and D. A. Savic. 2006. “A symbolic data-driven technique based on evolutionary polynomial regression.” J. Hydroinf. 8 (3): 207–222. https://doi.org/10.2166/hydro.2006.020b.
Golmohamadi, M. 2015. “Pipeline leak detection.” M.S. thesis, Dept. of Electrical and Computer Engineering, Missouri Univ. of Science and Technology.
Gutmann, H.-M. 2001. “A radial basis function method for global optimization.” J. Global Optim. 19 (3): 201–227. https://doi.org/10.1023/A:1011255519438.
Hu, X., Y. Han, B. Yu, Z. Geng, and J. Fan. 2021a. “Novel leakage detection and water loss management of urban water supply network using multiscale neural networks.” J. Cleaner Prod. 278 (Jan): 123611. https://doi.org/10.1016/j.jclepro.2020.123611.
Hu, Z., B. Chen, W. Chen, D. Tan, and D. Shen. 2021b. “Review of model-based and data-driven approaches for leak detection and location in water distribution systems.” Water Supply 21 (7): 3282–3306. https://doi.org/10.2166/ws.2021.101.
Hunaidi, O., W. Chu, A. Wang, and W. Guan. 2000. “Detecting leaks in plastic pipes.” J. Am. Water Works Assn. 92 (2): 82–94. https://doi.org/10.1002/j.1551-8833.2000.tb08819.x.
Hunaidi, O., and P. Giamou. 1998. “Ground-penetrating radar for detection of leaks in buried plastic water distribution pipes.” In Proc., Int. Conf. on Ground Penetrating Radar. Ottawa: National Research Council.
Jacobsz, S. W., and S. I. Jahnke. 2020. “Leak detection on water pipelines in unsaturated ground by discrete fibre optic sensing.” Struct. Health Monit. 19 (4): 1219–1236. https://doi.org/10.1177/1475921719881979.
Jung, D., D. Kang, J. Liu, and K. Lansey. 2015. “Improving the rapidity of responses to pipe burst in water distribution systems: A comparison of statistical process control methods.” J. Hydroinf. 17 (2): 307–328. https://doi.org/10.2166/hydro.2014.101.
Kang, D., and K. Lansey. 2009. “Real-time demand estimation and confidence limit analysis for water distribution systems.” J. Hydraul. Eng. 135 (10): 825–837. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000086.
Kang, J., Y.-J. Park, J. Lee, S.-H. Wang, and D.-S. Eom. 2017. “Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems.” IEEE Trans. Ind. Electron. 65 (5): 4279–4289. https://doi.org/10.1109/TIE.2017.2764861.
Kayaalp, F., A. Zengin, R. Kara, and S. Zavrak. 2017. “Leakage detection and localization on water transportation pipelines: A multi-label classification approach.” Neural Comput. Appl. 28 (10): 2905–2914. https://doi.org/10.1007/s00521-017-2872-4.
Laucelli, D., M. Romano, D. Savić, and O. Giustolisi. 2016. “Detecting anomalies in water distribution networks using EPR modelling paradigm.” J. Hydroinf. 18 (3): 409–427. https://doi.org/10.2166/hydro.2015.113.
Lay-Ekuakille, A., G. Vendramin, and A. Trotta. 2009. “Robust spectral leak detection of complex pipelines using filter diagonalization method.” IEEE Sens. J. 9 (11): 1605–1614. https://doi.org/10.1109/JSEN.2009.2027410.
Levenson, M., and B. Daley. 2010. A ‘catastrophic’ rupture hits region’s water system. Boston: Boston Globe.
Li, R., H. Huang, K. Xin, and T. Tao. 2015. “A review of methods for burst/leakage detection and location in water distribution systems.” Water Sci. Technol. Water Supply 15 (3): 429–441.
Lin, H., H. Lin, X. Fang, M. Wang, and L. Huang. 2020. “Intelligent pipeline leak detection and analysis system.” In Proc., 15th Int. Conf. on Computer Science & Education (ICCSE), 206–210. New York: IEEE.
Liu, Z., and Y. Kleiner. 2013. “State of the art review of inspection technologies for condition assessment of water pipes.” Measurement 46 (1): 1–15. https://doi.org/10.1016/j.measurement.2012.05.032.
Madabhushi, S., M. Elshafie, and S. Haigh. 2015. “Accuracy of distributed optical fiber temperature sensing for use in leak detection of subsea pipelines.” J. Pipeline Syst. Eng. Pract. 6 (2): 04014014. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000189.
Makar, J., and Y. Kleiner. 2000. “Maintaining water pipeline integrity.” In Proc., AWWA Infrastructure Conf. and Exhibition, 1–13. Ottawa: National Research Council.
Mamo, T. G. 2014. “Virtual district metered area municipal water supply pipeline leak detection and classification using advance pattern recognizer multi-class support vector machine for risk-based asset management.” Ph.D. thesis, Thewodros G. Mamo Dept. of Civil & Urban Engineering, Polytechnic Institute of New York Univ.
Mao, M., and X. Wang. 2016. “Interference signal analysis for leak detection in long-distance transmitting mineral slurry pipeline.” In Proc., 2016 Chinese Control and Decision Conf. (CCDC), 59–64. New York: IEEE.
Martini, A., M. Troncossi, and A. Rivola. 2017a. “Leak detection in water-filled small-diameter polyethylene pipes by means of acoustic emission measurements.” Appl. Sci. 7 (1): 2. https://doi.org/10.3390/app7010002.
Martini, A., M. Troncossi, and A. Rivola. 2017b. “Vibroacoustic measurements for detecting water leaks in buried small-diameter plastic pipes.” J. Pipeline Syst. Eng. Pract. 8 (4): 04017022. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000287.
Mashford, J., D. De Silva, S. Burn, and D. Marney. 2012. “Leak detection in simulated water pipe networks using SVM.” Appl. Artif. Intell. 26 (5): 429–444. https://doi.org/10.1080/08839514.2012.670974.
Mashford, J., D. De Silva, D. Marney, and S. Burn. 2009. “An approach to leak detection in pipe networks using analysis of monitored pressure values by support vector machine.” In Proc., 2009 3rd Int. Conf. on Network and System Security, 534–539. New York: IEEE.
Meniconi, S., B. Brunone, M. Ferrante, C. Capponi, C. Carrettini, C. Chiesa, D. Segalini, and E. Lanfranchi. 2015. “Anomaly pre-localization in distribution–transmission mains by pump trip: Preliminary field tests in the Milan pipe system.” J. Hydroinf. 17 (3): 377–389. https://doi.org/10.2166/hydro.2014.038.
Meseguer, J., J. M. Mirats-Tur, G. Cembrano, and V. Puig. 2015. “Model-based monitoring techniques for leakage localization in distribution water networks.” Procedia Eng. 119: 1399–1408. https://doi.org/10.1016/j.proeng.2015.08.1000.
Misiunas, D., J. Vítkovskỳ, G. Olsson, M. Lambert, and A. Simpson. 2006. “Failure monitoring in water distribution networks.” Water Sci. Technol. 53 (4–5): 503–511. https://doi.org/10.2166/wst.2006.154.
Misiunas, D., J. Vítkovskỳ, G. Olsson, A. Simpson, and M. Lambert. 2004. “Burst detection and location in pipe networks using a continuous monitoring technique.” In Proc., 9th Int. Conf. on Pressure Surges, 24–26. Chester, UK: BHR Group.
Mounce, S., J. Boxall, and J. Machell. 2010. “Development and verification of an online artificial intelligence system for detection of bursts and other abnormal flows.” J. Water Resour. Plann. Manage. 136 (3): 309–318. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000030.
Mounce, S., R. Mounce, T. Jackson, J. Austin, and J. Boxall. 2014. “Pattern matching and associative artificial neural networks for water distribution system time series data analysis.” J. Hydroinf. 16 (3): 617–632. https://doi.org/10.2166/hydro.2013.057.
Mounce, S. R., R. B. Mounce, and J. B. Boxall. 2011. “Novelty detection for time series data analysis in water distribution systems using support vector machines.” J. Hydroinf. 13 (4): 672–686. https://doi.org/10.2166/hydro.2010.144.
Murvay, P.-S., and I. Silea. 2012. “A survey on gas leak detection and localization techniques.” J. Loss Prev. Process Ind. 25 (6): 966–973. https://doi.org/10.1016/j.jlp.2012.05.010.
Obeid, A. M., F. Karray, M. W. Jmal, M. Abid, S. M. Qasim, and M. S. BenSaleh. 2016. “Towards realisation of wireless sensor network-based water pipeline monitoring systems: A comprehensive review of techniques and platforms.” IET Sci. Measur. Technol. 10 (5): 420–426. https://doi.org/10.1049/iet-smt.2015.0255.
Okeya, I., Z. Kapelan, C. Hutton, and D. Naga. 2014. “Online burst detection in a water distribution system using the Kalman filter and hydraulic modeling.” Procedia Eng. 89: 418–427. https://doi.org/10.1016/j.proeng.2014.11.207.
Ozevin, D., and H. Yalcinkaya. 2012. “Reliable monitoring of leak in gas pipelines using acoustic emission method.” In Proc., Civil Structural Health Monitoring Workshop, 1–8. Winnipeg, MB, Canada: Univ. of Manitoba.
Palau, C., F. Arregui, and M. Carlos. 2012. “Burst detection in water networks using principal component analysis.” J. Water Resour. Plann. Manage. 138 (1): 47–54. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000147.
Pavić, G. 1992. “Vibroacoustical energy flow through straight pipes.” J. Sound Vib. 154 (3): 411–429. https://doi.org/10.1016/0022-460X(92)90776-T.
Perez, R., G. Sanz, V. Puig, J. Quevedo, M. A. C. Escofet, F. Nejjari, J. Meseguer, G. Cembrano, J. M. M. Tur, and R. Sarrate. 2014. “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. https://doi.org/10.1109/MCS.2014.2320336.
Quevedo Casín, J. J., M. À. Cugueró Escofet, R. Pérez Magrané, F. Nejjari Akhi-Elarab, V. Puig Cayuela, and J. M. Mirats Tur. 2011. “Leakage location in water distribution networks based on correlation measurement of pressure sensors.” In Proc., 8th IWA Symp. on Systems Analysis and Integrated Assessment. London: International Water Association.
Rahimi, M., A. Alghassi, M. Ahsan, and J. Haider. 2020. “Deep learning model for industrial leakage detection using acoustic emission signal.” Informatics 7 (4): 49. https://doi.org/10.3390/informatics7040049.
Rai, A., and J.-M. Kim. 2021. “A novel pipeline leak detection approach independent of prior failure information.” Measurement 167 (Jan): 108284. https://doi.org/10.1016/j.measurement.2020.108284.
Rashid, S., U. Akram, and S. A. Khan. 2015. “WML: Wireless sensor network based machine learning for leakage detection and size estimation.” Procedia Comput. Sci. 63: 171–176. https://doi.org/10.1016/j.procs.2015.08.329.
Ravichandran, T., K. Gavahi, K. Ponnambalam, V. Burtea, and S. J. Mousavi. 2021. “Ensemble-based machine learning approach for improved leak detection in water mains.” J. Hydroinf. 23 (2): 307–323. https://doi.org/10.2166/hydro.2021.093.
Romano, M., Z. Kapelan, and D. Savić. 2010. “Real-time leak detection in water distribution systems.” In Proc., 12th Annual Conf. on Water Distribution Systems Analysis, 1074–1082. Reston, VA: ASCE.
Romano, M., Z. Kapelan, and D. A. Savić. 2014. “Automated detection of pipe bursts and other events in water distribution systems.” J. Water Resour. Plann. Manage. 140 (4): 457–467. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000339.
Saghi, H., and A. A. Aval. 2015. “Effective factors in causing leakage in water supply systems and urban water distribution networks.” Am. J. Civ. Eng. 3 (2): 60. https://doi.org/10.11648/j.ajce.s.2015030202.22.
Sanz, G., R. Pérez, Z. Kapelan, and D. Savic. 2016. “Leak detection and localization through demand components calibration.” J. Water Resour. Plann. Manage. 142 (2): 04015057. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000592.
Scott, S. L., and M. A. Barrufet. 2003. Worldwide assessment of industry leak detection capabilities for single & multiphase pipelines. College Station, TX: Offshore Technology Research Center College Station.
Shakmak, B., and A. Al-Habaibeh. 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 Conf. on Applied Electrical Engineering and Computing Technologies (AEECT), 1–7. New York: IEEE.
Shekofteh, M., M. Jalili Ghazizadeh, and J. Yazdi. 2020. “A methodology for leak detection in water distribution networks using graph theory and artificial neural network.” Urban Water J. 17 (6): 525–533. https://doi.org/10.1080/1573062X.2020.1797832.
Shijiu, J., W. Lining, and L. Jian. 1998. “Instantaneous negative pressure wave pattern recognition method in leak detection of crude petroleum transported pipeline.” J. Electron. Measur. Instroment 12 (1): 59–64. https://doi.org/10.13382/j.jemi.1998.01.012.
Smith, L. A., K. A. Fields, A. S. Chen, and A. N. Tafuri. 2000. “Options for leak and break detection and repair of drinking water systems. Columbus, OH: Battelle Press.
Specht, D. F. 1990. “Probabilistic neural networks.” Neural Netw. 3 (1): 109–118. https://doi.org/10.1016/0893-6080(90)90049-Q.
Srirangarajan, S., M. Allen, A. Preis, M. Iqbal, H. B. Lim, and A. J. Whittle. 2013. “Wavelet-based burst event detection and localization in water distribution systems.” J. Signal Process. Syst. 72 (1): 1–16. https://doi.org/10.1007/s11265-012-0690-6.
Taïeb, L. H., L. Ayed, and E. H. Taïeb. 2012. “Leak detection in viscoelastic pipe by transient analysis.” In Condition monitoring of machinery in non-stationary operations, 69–79. New York: Springer.
Tsoumakas, G., and I. Vlahavas. 2007. “Random k-labelsets: An ensemble method for multilabel classification.” In Proc., European Conf. on Machine Learning, 406–417. New York: Springer.
Vrachimis, S. G., and M. S. Kyriakou. 2018. “LeakDB: A benchmark dataset for leakage diagnosis in water distribution networks.” In Vol. 1 of Proc., 1st Int. WDSA/CCWI Joint Conf. Kingston, ON, Canada: Queen’s Univ.
Wan, J., Y. Yu, Y. Wu, R. Feng, and N. Yu. 2012. “Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks.” Sensors 12 (1): 189–214. https://doi.org/10.3390/s120100189.
Wang, X., G. Guo, S. Liu, Y. Wu, X. Xu, and K. Smith. 2020. “Burst detection in district metering areas using deep learning method.” J. Water Resour. Plann. Manage. 146 (6): 04020031. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001223.
Wu, Y., and S. Liu. 2017. “A review of data-driven approaches for burst detection in water distribution systems.” Urban Water J. 14 (9): 972–983. https://doi.org/10.1080/1573062X.2017.1279191.
Wu, Z. Y., P. Sage, and D. Turtle. 2010. “Pressure-dependent leak detection model and its application to a district water system.” J. Water Resour. Plann. Manage. 136 (1): 116–128. https://doi.org/10.1061/(ASCE)0733-9496(2010)136:1(116).
Wu, Z. Y., and A. R. Simpson. 2001. “Competent genetic-evolutionary optimization of water distribution systems.” J. Comput. Civ. Eng. 15 (2): 89–101. https://doi.org/10.1061/(ASCE)0887-3801(2001)15:2(89).
Xu, T., Z. Zeng, X. Huang, J. Li, and H. Feng. 2021. “Pipeline leak detection based on variational mode decomposition and support vector machine using an interior spherical detector.” Process Saf. Environ. Prot. 153 (Sep): 167–177. https://doi.org/10.1016/j.psep.2021.07.024.
Yang, Z., Z. Xiong, and M. Shao. 2010. “A new method of leak location for the natural gas pipeline based on wavelet analysis.” Energy 35 (9): 3814–3820.
Ye, G., and R. A. Fenner. 2011. “Kalman filtering of hydraulic measurements for burst detection in water distribution systems.” J. Pipeline Syst. Eng. Pract. 2 (1): 14–22. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000070.
Ye, G., and R. A. Fenner. 2014. “Weighted least squares with expectation-maximization algorithm for burst detection in UK water distribution systems.” J. Water Resour. Plann. Manage. 140 (4): 417–424. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000344.
Yu, J., L. Zhang, J. Chen, Y. Xiao, D. Hou, P. Huang, G. Zhang, and H. Zhang. 2021. “An integrated bottom-up approach for leak detection in water distribution networks based on assessing parameters of water balance model.” Water 13 (6): 867. https://doi.org/10.3390/w13060867.
Zadkarami, M., M. Shahbazian, and K. Salahshoor. 2017. “Pipeline leak diagnosis based on wavelet and statistical features using Dempster–Shafer classifier fusion technique.” Process Saf. Environ. Prot. 105 (Jan): 156–163. https://doi.org/10.1016/j.psep.2016.11.002.
Zan, T. T. T., H. B. Lim, K.-J. Wong, A. J. Whittle, and B.-S. Lee. 2014. “Event detection and localization in urban water distribution network.” IEEE Sens. J. 14 (12): 4134–4142. https://doi.org/10.1109/JSEN.2014.2358842.
Zhang, Q., Z. Y. Wu, M. Zhao, J. Qi, Y. Huang, and H. Zhao. 2016. “Leakage zone identification in large-scale water distribution systems using multiclass support vector machines.” J. Water Resour. Plann. Manage. 142 (11): 04016042. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000661.
Zhang, T., Y. Tan, X. Zhang, and J. Zhao. 2015. “A novel hybrid technique for leak detection and location in straight pipelines.” J. Loss Prev. Process Ind. 35 (May): 157–168. https://doi.org/10.1016/j.jlp.2015.04.012.
Zhang, Z., H. Ye, G. Wang, and J. Yang. 2005. “Leak detection in transport pipelines using enhanced independent component analysis and support vector machines.” In Vol. 3611 of Proc., Int. Conf. on Natural Computation, edited by L. Wang, K. Chen, and Y. S. Ong, 95–100. Berlin: Springer. https://doi.org/10.1007/11539117_16.
Zhou, B., V. Lau, and X. Wang. 2019. “Machine-learning-based leakage-event identification for smart water supply systems.” IEEE Internet Things J. 7 (3): 2277–2292. https://doi.org/10.1109/JIOT.2019.2958920.
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Journal of Pipeline Systems Engineering and Practice
Volume 13 • Issue 3 • August 2022
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Received: Dec 11, 2020
Accepted: Jan 14, 2022
Published online: May 27, 2022
Published in print: Aug 1, 2022
Discussion open until: Oct 27, 2022
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- Nurfarah Anisah Mohd Yussof, Hann Woei Ho, Review of Water Leak Detection Methods in Smart Building Applications, Buildings, 10.3390/buildings12101535, 12, 10, (1535), (2022).
- Maryam Kammoun, Amina Kammoun, Mohamed Abid, LSTM-AE-WLDL: Unsupervised LSTM Auto-Encoders for Leak Detection and Location in Water Distribution Networks, Water Resources Management, 10.1007/s11269-022-03397-6, 37, 2, (731-746), (2022).