Clustering-Learning Approach to the Localization of Leaks in Water Distribution Networks
Publication: Journal of Water Resources Planning and Management
Volume 148, Issue 4
Abstract
Leak detection and localization in water distribution networks (WDNs) is of great significance for water utilities. This paper proposes a leak localization method that requires hydraulic measurements and structural information of the network. It is composed by an image encoding procedure and a recursive clustering/learning approach. Image encoding is carried out using Gramian angular field (GAF) on pressure measurements to obtain images for the learning phase (for all possible leak scenarios). The recursive clustering/learning approach divides the considered region of the network into two sets of nodes using graph agglomerative clustering (GAC) and trains a deep neural network (DNN) to discern the location of each leak between the two possible clusters, using each one of them as inputs to future iterations of the process. The achieved set of DNNs is hierarchically organized to generate a classification tree. Actual measurements from a leak event occurred in a real network are used to assess the approach, comparing its performance with another state-of-the-art technique, and demonstrating the capability of the method to regulate the area of localization depending on the depth of the route through the tree.
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Data Availability Statement
All data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. Specifically, there is a hard restriction in the sharing of information about the studied network: features, location, the hydraulic information from the real leak event. The associated codes could be shared upon reasonable request.
Acknowledgments
The authors want to thank the Spanish national project “DEOCS (DPI2016-76493-C3-3-R)” project (which is finished nowadays) by its continuation: “L-BEST Project (PID2020-115905RB-C21) funded by MCIN/ AEI /10.13039/501100011033” and the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). Joaquim Blesa acknowledges the support from the Serra Húnter program.
References
Agarap, A. 2018. “Deep learning using rectified linear units (relu).” Preprint, submitted March 22, 2018. http://arxiv.org/1803.08375.
Arifin, B., Z. Li, S. Shaha, G. Meyer, and A. Colin. 2018. “A novel data-driven leak detection and localization algorithm using the Kantorovich distance.” Comput. Chem. Eng. 108 (Jan): 300–313. https://doi.org/10.1016/j.compchemeng.2017.09.022.
Bishop, C. 2006. “Pattern recognition and machine learning.” In Probability distributions—The exponential family. New York: Springer.
Blesa, J., and R. Pérez. 2018. “Modelling uncertainty for leak localization in water networks.” IFAC-PapersOnLine 51 (24): 730–735. https://doi.org/10.1016/j.ifacol.2018.09.656.
Casillas, M., L. Garza-Castañón, and V. Puig. 2013. “Model-based leak detection and location in water distribution networks considering an extended-horizon analysis of pressure sensitivities.” J. Hydroinf. 16 (3): 649–670. https://doi.org/10.2166/hydro.2013.019.
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 (Dec): 78846–78867. https://doi.org/10.1109/ACCESS.2018.2885444.
Festa, P. 2006. “Shortest path algorithms.” In Handbook of optimization in telecommunications, edited by M. Resende and P. Pardalos, 185–210. Boston: Springer.
Han, Q., R. Eguchi, S. Mehrotra, and N. Venkatasubramanian. 2018. “Enabling state estimation for fault identification in water distribution systems under large disasters.” In Proc., IEEE 37th Symp. on Reliable Distributed Systems (SRDS), 161–170. New York: IEEE. https://doi.org/10.1109/SRDS.2018.00027.
Hastie, T., R. Tibshirani, and J. Friedman. 2009. The elements of statistical learning. Data mining, inference, and prediction. Berlin: Springer.
Huang, Y., F. Zheng, Z. Kapelan, D. Savić, H.-F. Duan, and Q. Zhang. 2020. “Efficient leak localization in water distribution systems using multistage optimal valve operations and smart demand metering.” Water Resour. Res. 56 (10): e2020WR028285. https://doi.org/10.1029/2020WR028285.
Ioffe, S., and C. Szegedy. 2015. “Batch normalization: Accelerating deep network training by reducing internal covariate shift.” Preprint, submitted February 11, 2015. https://arxiv.org/abs/1502.03167.
Javadiha, M., J. Blesa, A. Soldevila, and V. Puig. 2019. “Leak localization in water distribution networks using deep learning.” In Proc., 2019 6th Int. Conf. on Control, Decision, and Information Technologies (CoDIT), 1426–1431. New York: IEEE. https://doi.org/10.1109/CoDIT.2019.8820627.
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.
LeChevallier, M. W., R. W. Gullick, M. R. Karim, M. Friedman, and J. E. Funk. 2003. “The potential for health risks from intrusion of contaminants into the distribution system from pressure transients.” J. Water Health 1 (1): 3–14. https://doi.org/10.2166/wh.2003.0002.
Menapace, A., D. Avesani, M. Righetti, A. Bellin, and G. Pisaturo. 2018. “Uniformly distributed demand EPANET extension.” Water Resour. Manage. 32 (6): 2165–2180. https://doi.org/10.1007/s11269-018-1924-6.
Murphy, J. 2016. An overview of convolutional neural network architectures for deep learning. Plymouth, MA: Microway.
Navarro, A., O. Begovich, J. Delgado-Aguiñaga, and J. Sánchez. 2019. “Real time leak isolation in pipelines based on a time delay neural network.” In Proc., 2019 IEEE Int. Autumn Meeting on Power, Electronics and Computing (ROPEC), 1–6. New York: IEEE. https://doi.org/10.1109/ROPEC48299.2019.9057112.
Pemmaraju, S., and S. Skiena. 2003. “Computational discrete mathematics: Combinatorics and graph theory with mathematica®.” In Strong and weak connectivity—Connectivity—Properties of graphs, edited by M. Resende and P. Pardalos. Cambridge, UK: Cambridge University Press.
Pérez, R., G. Sanz, V. Puig, J. Quevedo, M. À. Cugueró-Escofet, F. Nejjari, J. Meseguer, G. Cembrano, J. M. Mirats-Tur, and R. Sarrate. 2014. “Leak localization in water networks: A model-based methodology using pressure sensors applied to a real network in Barcelona.” IEEE Control Syst. 34 (4): 24–36. https://doi.org/10.1109/MCS.2014.2320336.
Pérez-Pérez, E., F. López-Estrada, G. Valencia-Palomo, L. Torres, V. Puig, and J. Mina-Antonio. 2021. “Leak diagnosis in pipelines using a combined artificial neural network approach.” Control Eng. Pract. 107 (Feb): 104677. https://doi.org/10.1016/j.conengprac.2020.104677.
Puust, R., Z. Kapelan, D. Savić, and T. Koppel. 2010. “A review of methods for leakage management in pipe networks.” Urban Water J. 7 (1): 25–45. https://doi.org/10.1080/15730621003610878.
Qian, N. 1999. “On the momentum term in gradient descent learning algorithms.” Neural Networks 12 (1): 145–151. https://doi.org/10.1016/S0893-6080(98)00116-6.
Rossman, L. A. 2000. EPANET 2 user’s manual. Cincinnati: National Risk Management Research Laboratory.
Sanz, G., R. Pérez, Z. Kapelan, and D. Savić. 2015. “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.
Savić, D., and G. Ferrari. 2014. “Design and performance of district metering areas in water distribution systems.” Procedia Eng. 89: 1136–1143. https://doi.org/10.1016/j.proeng.2014.11.236.
Savić, D. A., Z. S. Kapelan, and P. M. Jonkergouw. 2009. “Quo vadis water distribution model calibration?” Urban Water J. 6 (1): 3–22. https://doi.org/10.1080/15730620802613380.
Soldevila, A., J. Blesa, T. N. Jensen, S. Tornil-Sin, R. M. Fernández-Cant, and V. Puig. 2020. “Leak localization method for water-distribution networks using a data-driven model and Dempster-Shafer reasoning.” IEEE Trans. Control Syst. Technol. 29 (3): 937–948. https://doi.org/10.1109/TCST.2020.2982349.
Sophocleous, S., D. Savić, and Z. Kapelan. 2019. “Leak localization in a real water distribution network based on search-space reduction.” J. Water Resour. Plann. Manage. 145 (7): 04019024. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001079.
Wang, D., Y. Zheng, and J. Cao. 2012. “Parallel construction of approximate KNN graph.” In Proc., 2012 11th Int. Symposium on Distributed Computing and Applications to Business, Engineering & Science, 22–26. New York: IEEE. https://doi.org/10.1109/DCABES.2012.87.
Wang, Z., and T. Oates. 2015. “Imaging time-series to improve classification and imputation.” In Proc., 24th Int. Joint Conf. on Artificial Intelligence, 3939–3945. Buenos Aires, Argentina: IJCAI.
Xu, Q., R. Liu, Q. Chen, and R. Li. 2014. “Review on water leakage control in distribution networks and the associated environmental benefits.” J. Environ. Sci. 26 (5): 955–961. https://doi.org/10.1016/S1001-0742(13)60569-0.
Zhang, W., D. Zhao, and X. Wang. 2013. “Agglomerative clustering via maximum incremental path integral.” Pattern Recognit. 46 (11): 3056–3065. https://doi.org/10.1016/j.patcog.2013.04.013.
Zhou, X., Z. Tang, W. Xu, F. Meng, X. Chu, K. Xin, and G. Fu. 2019. “Deep learning identifies accurate burst locations in water distribution networks.” Water Res. 166 (Dec): 115058. https://doi.org/10.1016/j.watres.2019.115058.
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Journal of Water Resources Planning and Management
Volume 148 • Issue 4 • April 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Nov 24, 2020
Accepted: Nov 16, 2021
Published online: Jan 27, 2022
Published in print: Apr 1, 2022
Discussion open until: Jun 27, 2022
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