Abstract
Water distribution networks (WDNs) are vital infrastructure which serve as a means for public utilities to deliver potable water to consumers. Naturally, pipelines degrade over time, causing leakages and pipe bursts. Damaged pipelines allow water to leak through, incurring significant economic losses. Mitigating these losses are important, especially in areas with water scarcity, to allow consumers to have adequate water supply. Globally, as the population increases, there is a need to make water distribution efficient, due to the rising demand. Thus, leak detection is vital for reducing the system loss of the network and improving efficiency.
Monitoring WDNs effectively for leakage is often a challenging task due to the size of the area it covers, and due to the need to detect leaks as early as possible. Traditionally, this is done via pipeline inspection or physical modeling. However, such techniques are either time-consuming, resource intensive, or both. An alternative is machine learning (ML), which maps the relationship between pipeline data to detect leakages. This allows for a faster, yet reasonably accurate model for detection and localization. Machine learning techniques could be utilized together as an ensemble, which allows these techniques to work in conjunction with each other. Wavelet decomposition will be performed on the data to allow for a smaller dataset, as well as utilizing possible hidden features for the machine learning model.
This work is supported by a Commission on Higher Education (CHED) Philippine California Advanced Research Institues (PCARI) grant under Project IIID54 - Resilient Cyber Physical Societal Scale Systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adedeji, K.B., Hamam, Y., Abe, B.T., Abu-Mahfouz, A.M.: Towards achieving a reliable leakage detection and localization algorithm for application in water piping networks: an overview. IEEE Access 5, 20272–20285 (2017). https://doi.org/10.1109/access.2017.2752802
Wan, J., Yu, Y., Wu, Y., Feng, R., Yu, N.: Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks. Sensors 12(12), 189–214 (2011). https://doi.org/10.3390/s120100189
He, Y., Li, S., Zheng, Y.: Distributed state estimation for leak detection in water supply networks. IEEE/CAA J. Autom. Sin. PP(99), 1–9 (2017). https://doi.org/10.1109/JAS.2017.7510367
Gupta, G.: Monitoring Water Distribution Network using Machine Learning. KTH Royal Institute of Sweden, Stockholm (2017)
Xu, Q., Liu, R., Chen, Q., Li, R.: Review on water leakage control in distribution networks and the associated environmental benefits. J. Environ. Sci. (China) 26(5), 955–961 (2014). https://doi.org/10.1016/S1001-0742(13)60569-0
Conejos, M.P., Alzamora, F.M., Alonso, J.C.: A water distribution system model to simulate critical scenarios by considering both leakage and pressure dependent demands. Procedia Eng. 186, 380–387 (2017). https://doi.org/10.1016/j.proeng.2017.03.234
EPANET. https://www.epa.gov/water-research/epanet. Accessed 23 May 2018
Kang, D., Lansey, K.: Novel approach to detecting pipe bursts in water distribution networks. J. Water Resour. Plan. Manage. 140(1), 121–127 (2014). https://doi.org/10.1061/(ASCE)WR.1943-5452.0000264
Fang, C.M., Lin, S.C.: 5.4 GHz high-Q bandpass filter for wireless sensor network system. In: IEEE SENSORS 2009 Conference (2009). https://doi.org/10.1109/ICSENS.2009.5398458
Colombo, A.F., Lee, P., Karney, B.W.: A selective literature review of transient-based leak detection methods. J. Hydro-Environ. Res. 2(4), 212–227 (2009). https://doi.org/10.1016/j.jher.2009.02.003
Wang, X., Ghidaoui, M.S.: Identification of multiple leaks in pipeline: linearized model, maximum likelihood, and super-resolution localization. Mech. Syst. Signal Process. 107, 529–548 (2018). https://doi.org/10.1016/j.ymssp.2018.01.042
Quiñones-Grueiro, M., Verde, C., Llanes-Santiago, O.: Demand model in water distribution networks for fault detection. IFAC-PapersOnLine 50(1), 3263–3268 (2017). https://doi.org/10.1016/j.ifacol.2017.08.460
Goyal, M.K., Ojha, C.S.P., Burn, D.H.: Machine learning algorithms and their application in water resources management. In: Sustainable Water Resources Management, pp. 165–177. American Society of Civil Engineers (2017). https://doi.org/10.1061/9780784414767.ch06
Kang, J., Park, Y., Lee, J., Wang, S., Eom, D.: Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems. IEEE Trans. Ind. Electron. 65(5), 4279–4289 (2018). https://doi.org/10.1109/TIE.2017.2764861
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45014-9_1
Shi, F., Liu, Z., Li, E.: Prediction of pipe performance with ensemble machine learning based approaches. In: 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), Shanghai, pp. 408–414 (2017). https://doi.org/10.1109/SDPC.2017.84
Nasir, M.T., Mysorewala, M., Cheded, L., Siddiqui, B., Sabih, M.: Measurement error sensitivity analysis for detecting and locating leak in pipeline using ANN and SVM. In: 2014 IEEE 11th International Multi-Conference System Signals Devices, SSD 2014, pp. 7–10 (2014). https://doi.org/10.1109/SSD.2014.6808847
Gupta, K., Kishore, K., Jain, S.C.: Modeling and simulation of CEERI’s water distribution network to detect leakage using HLR approach. In: 2017 6th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions) (2017). https://doi.org/10.1109/ICRITO.2017.8342440
Rossman, L.A., Clark, R.M., Grayman, W.M.: Modeling chlorine residuals in drinking-water distribution systems. J. Environ. Eng. 120(4), 803–820 (1994). https://doi.org/10.1061/(ASCE)0733-9372(1994)120:4(803)
University of Exeter Centre for Water Systems. http://emps.exeter.ac.uk/engineering/research/cws/resources/benchmarks/. Accessed 23 May 2018
Delorme-Costil, A., Bezian, J.J.: Forecasting domestic hot water demand in residential house using artificial neural networks. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, pp. 467–472 (2017). https://doi.org/10.1109/ICMLA.2017.0-117
Domene, E., Sauri, D.: Urbanisation and water consumption: influencing factors in the metropolitan region of Barcelona. Urban Stud. 43(9), 1605–1623 (2006). https://doi.org/10.1080/00420980600749969
Letting, L.K., Hamam, Y., Abu-Mahfouz, A.M.: Estimation of water demand in water distribution systems using particle swarm optimization. Water, 9(8) (2017). https://doi.org/10.3390/w9080593
Fagiani, M., Squartini, S., Severini, M., Piazza, F.: A novelty detection approach to identify the occurrence of leakage in smart gas and water grids. In: Proceedings of the International Joint Conference Neural Networks, vol. 2015, September 2015. https://doi.org/10.1109/IJCNN.2015.7280473
Wu, Z.Y., El-Maghraby, M., Pathak, S.: Applications of deep learning for smart water networks. Procedia Eng. 119, 479–485 (2015). https://doi.org/10.1016/j.proeng.2015.08.870
Xu, Y., Zhang, J., Long, Z., Chen, Y.: A novel dual-scale deep belief network method for daily urban water demand forecasting. Energies 11(5), 1068 (2018). https://doi.org/10.3390/en11051068
Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manage. 45(4), 427–437 (2009). https://doi.org/10.1016/j.ipm.2009.03.002
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Fuentes, V.C., Pedrasa, J.R.I. (2020). Leak Detection in Water Distribution Networks via Pressure Analysis Using a Machine Learning Ensemble. In: Pereira, P., Ribeiro, R., Oliveira, I., Novais, P. (eds) Society with Future: Smart and Liveable Cities. SC4Life 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 318. Springer, Cham. https://doi.org/10.1007/978-3-030-45293-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-45293-3_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45292-6
Online ISBN: 978-3-030-45293-3
eBook Packages: Computer ScienceComputer Science (R0)