Pressure Sensor Placement for Leakage Detection and Calibration of Water Distribution Networks Based on Multiview Clustering and Global Sensitivity Analysis
Publication: Journal of Water Resources Planning and Management
Volume 150, Issue 5
Abstract
Most previous studies in the field of sensor placement have focused on only one aim. In this study, pressure sensor placement is done for calibration and leakage detection simultaneously to use pressure data optimally to save money and time. The sensor placement method implemented in this paper consists of two main parts: clustering the nodes of a water distribution network (WDN) and determining the representative node of each cluster as the sensors’ location. A new multiview clustering approach is implemented to cluster nodes of a WDN based on two pressure sensitivity matrices. In fact, two different aspects of nodes’ characteristics are used for clustering. The representative node from each cluster is also chosen based on global sensitivity and the number of detection criteria. The Sobol method is used for global sensitivity analysis, and the number of detections is calculated with the local sensitivity matrices. The performance of sensor placement is evaluated individually and collectively for different goals in the Anytown network. The accuracy of network calibration with the sample design proposed in this study is equal to 0.0707 m, which is the lowest value between previous studies. Leakage detection also has a significant performance ratio than random pressure sampling. Furthermore, this new method of sensor placement is evaluated in the more extensive networks of the C-town and the Modena, which have high complexity. The performance of the presented pressure sampling is significantly different from the random pressure sampling, and the pressure data collected at the selected nodes can be used for the calibration procedure efficiently.
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Data Availability Statement
Some or all of the data, models, or code generated or used in the study are available on request from the corresponding author. Additionally, the network data for the Anytown and the C-town are publicly accessible. The WDN analysis was conducted using the WNTR Python package, while the Sobol method employed the SALib Python package for sensitivity analysis (Klise et al. 2017; Herman and Usher 2017). The mvlearn Python package was utilized for clustering, and the Louvain community discovery algorithm was implemented using the NetworkX Python package (Perry et al. 2021; Hagberg et al. 2008).
References
Abbasi Moghaddam, V., and M. Tabesh. 2021. “Sampling design of hydraulic and quality model calibration based on a global sensitivity analysis method.” J. Water Resour. Plann. Manage. 147 (7): 04021035. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001390.
Behzadian, K., Z. Kapelan, D. Savic, and A. Ardeshir. 2009. “Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks.” Environ. Modell. Software 24 (4): 530–541. https://doi.org/10.1016/j.envsoft.2008.09.013.
Bickel, S., and T. Scheffer. 2004. “Multi-view clustering.” ICDM 4 (2004): 19–26.
Blondel, V. D., J. L. Guillaume, R. Lambiotte, and E. Lefebvre. 2008. “Fast unfolding of communities in large networks.” J. Stat. Mech. Theory Exp. 2008 (10): P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008.
Boatwright, S., S. Mounce, M. Romano, and J. Boxall. 2023. “Integrated sensor placement and leak localization using geospatial genetic algorithms.” J. Water Resour. Plann. Manage. 149 (9): 04023040. https://doi.org/10.1061/JWRMD5.WRENG-6037.
Brentan, B., S. Carpitella, D. Barros, G. Meirelles, A. Certa, and J. Izquierdo. 2021. “Water quality sensor placement: A multi-objective and multi-criteria approach.” Water Resour. Manage. 35 (1): 225–241. https://doi.org/10.1007/s11269-020-02720-3.
Bush, C. A., and J. G. Uber. 1998. “Sampling design methods for water distribution model calibration.” J. Water Resour. Plann. Manage. 124 (6): 334–344. https://doi.org/10.1061/(ASCE)0733-9496(1998)124:6(334).
Casillas, M. V., L. E. Garza-Castañón, and V. Puig. 2015. “Optimal sensor placement for leak location in water distribution networks using evolutionary algorithms.” Water 7 (11): 6496–6515. https://doi.org/10.3390/w7116496.
Casillas Ponce, M. V., L. E. Garza Castanon, and V. P. Cayuela. 2014. “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.
De Schaetzen, W. B. F., G. A. Walters, and D. A. Savic. 2000. “Optimal sampling design for model calibration using shortest path, genetic and entropy algorithms.” Urban Water 2 (2): 141–152. https://doi.org/10.1016/S1462-0758(00)00052-2.
Diao, K. 2020. “Multiscale resilience in water distribution and drainage systems.” Water 12 (6): 1521. https://doi.org/10.3390/w12061521.
Ding, C., and X. He. 2004. “K-means clustering via principal component analysis.” In Proc., 21st Int. Conf. on Machine Learning, 29. New York: Association for Computing Machinery. https://doi.org/10.1145/1015330.1015408.
Ferreira, B., A. Antunes, N. Carriço, and D. Covas. 2022. “Multi-objective optimization of pressure sensor location for burst detection and network calibration.” Comput. Chem. Eng. 162 (Apr): 107826. https://doi.org/10.1016/j.compchemeng.2022.107826.
Ferreri, G. B., E. Napoli, and A. Tumbiolo. 1994. “Calibration of roughness in water distribution networks.” In Proc., 2nd Int. Conf. on Water Pipeline Systems, 379–396. Edinburgh, UK: BHR Group.
Gamboa-Medina, M. M., and L. F. R. Reis. 2017. “Sampling design for leak detection in water distribution networks.” Procedia Eng. 186 (Jun): 460–469. https://doi.org/10.1016/j.proeng.2017.03.255.
Giudicianni, C., M. Herrera, A. Di Nardo, E. Creaco, and R. Greco. 2022. “Multi-criteria method for the realistic placement of water quality sensors on pipes of water distribution systems.” Environ. Modell. Software 152 (Sep): 105405. https://doi.org/10.1016/j.envsoft.2022.105405.
Giudicianni, C., M. Herrera, A. Di Nardo, R. Greco, E. Creaco, and A. Scala. 2020. “Topological placement of quality sensors in water-distribution networks without the recourse to hydraulic modeling.” J. Water Resour. Plann. Manage. 146 (6): 04020030 https://doi.org/10.1061/(ASCE)WR.1943-5452.0001210.
Hagberg, A., P. Swart, and D. S. Chult. 2008. Exploring network structure, dynamics, and function using NetworkX. Los Alamos, NM: Los Alamos National Lab.
Halko, N., P. G. Martinsson, and J. A. Tropp. 2011. “Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions.” SIAM Rev. 53 (2): 217–288. https://doi.org/10.1137/090771806.
Herman, J., and W. Usher. 2017. “SALib: An open-source Python library for sensitivity analysis.” J. Open Source Software 2 (9): 97. https://doi.org/10.21105/joss.00097.
Homma, T., and A. Saltelli. 1996. “Importance measures in global sensitivity analysis of nonlinear models.” Reliab. Eng. Syst. Saf. 52 (1): 1–17. https://doi.org/10.1016/0951-8320(96)00002-6.
Hu, C., M. Li, D. Zeng, and S. Guo. 2018. “A survey on sensor placement for contamination detection in water distribution systems.” Wireless Netw. 24 (2): 647–661. https://doi.org/10.1007/s11276-016-1358-0.
Iwanaga, T., W. Usher, and J. Herman. 2022. “Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses.” Socio-Environ. Syst. Modell. 4 (May): 18155. https://doi.org/10.18174/sesmo.18155.
Kapelan, Z. S., D. A. Savic, and G. A. Walters. 2003. “Multiobjective sampling design for water distribution model calibration.” J. Water Resour. Plann. Manage. 129 (6): 466–479. https://doi.org/10.1061/(ASCE)0733-9496(2003)129:6(466).
Khorshidi, M. S., M. R. Nikoo, N. Taravatrooy, M. Sadegh, M. Al-Wardy, and G. A. Al-Rawas. 2020. “Pressure sensor placement in water distribution networks for leak detection using a hybrid information-entropy approach.” Inf. Sci. 516 (Apr): 56–71. https://doi.org/10.1016/j.ins.2019.12.043.
Klise, K. A., M. Bynum, D. Moriarty, and R. Murray. 2017. “A software framework for assessing the resilience of drinking water systems to disasters with an example earthquake case study.” Environ. Modell. Software 95 (Sep): 420–431. https://doi.org/10.1016/j.envsoft.2017.06.022.
Mankad, J., B. Natarajan, and B. Srinivasan. 2022. “Integrated approach for optimal sensor placement and state estimation: A case study on water distribution networks.” ISA Trans. 123 (Apr): 272–285. https://doi.org/10.1016/j.isatra.2021.06.004.
Morosini, A. F., F. Costanzo, P. Veltri, and D. Savić. 2014. “Identification of measurement points for calibration of water distribution network models.” Procedia Eng. 89 (Jan): 693–701. https://doi.org/10.1016/j.proeng.2014.11.496.
Nejjari, F., R. Sarrate, and J. Blesa. 2015. “Optimal pressure sensor placement in water distribution networks minimizing leak location uncertainty.” Procedia Eng. 119 (Jan): 953–962. https://doi.org/10.1016/j.proeng.2015.08.979.
Ostfeld, A., et al. 2008. “The battle of the water sensor networks (BWSN): A design challenge for engineers and algorithms.” J. Water Resour. Plann. Manage. 134 (6): 556–568. https://doi.org/10.1061/(ASCE)0733-9496(2008)134:6(556).
Ostfeld, A. 2012. “Optimal reliable design and operation of water distribution systems through decomposition.” Water Resour. Res. 48 (10): W10521. https://doi.org/10.1029/2011WR011651.
Patel, P., B. Sivaiah, and R. Patel. 2022. “Approaches for finding optimal number of clusters using k-means and agglomerative hierarchical clustering techniques.” In Proc., 2022 Int. Conf. on Intelligent Controller and Computing for Smart Power (ICICCSP), 1–6. New York: IEEE. https://doi.org/10.1109/ICICCSP53532.2022.9862439.
Peng, S., J. Cheng, X. Wu, X. Fang, and Q. Wu. 2022. “Pressure sensor placement in water supply network based on graph neural network clustering method.” Water 14 (2): 150. https://doi.org/10.3390/w14020150.
Perry, R., G. Mischler, R. Guo, T. Lee, A. Chang, A. Koul, and J. T. Vogelstein. 2021. “mvlearn: Multiview machine learning in Python.” J. Mach. Learn. Res. 22 (1): 4938–4944. https://doi.org/10.5555/3546258.3546367.
Pianosi, F., K. Beven, J. Freer, J. W. Hall, J. Rougier, D. B. Stephenson, and T. Wagener. 2016. “Sensitivity analysis of environmental models: A systematic review with practical workflow.” Environ. Modell. Software 79 (Sep): 214–232. https://doi.org/10.1016/j.envsoft.2016.02.008.
Piller, O., J. Deuerlein, D. Gilbert, and J. M. Weber. 2015. “Installing fixed sensors for double calibration and early-warning detection purposes.” Procedia Eng. 119 (Jan): 564–572. https://doi.org/10.1016/j.proeng.2015.08.909.
Preis, A., A. Whittle, and A. Ostfeld. 2011. “Multi-objective optimization for conjunctive placement of hydraulic and water quality sensors in water distribution systems.” Water Sci. Technol. Water Supply 11 (2): 166–171. https://doi.org/10.2166/ws.2011.029.
Quiñones-Grueiro, M., M. A. Milián, M. S. Rivero, A. J. S. Neto, and O. Llanes-Santiago. 2021. “Robust leak localization in water distribution networks using computational intelligence.” Neurocomputing 438 (May): 195–208. https://doi.org/10.1016/j.neucom.2020.04.159.
Quiñones-Grueiro, M., C. Verde, and O. Llanes-Santiago. 2019. “Multi-objective sensor placement for leakage detection and localization in water distribution networks.” In Proc., 4th Conf. on Control and Fault Tolerant Systems, 129–134. New York: IEEE. https://doi.org/10.1109/SYSTOL.2019.8864746.
Raei, E., M. R. Nikoo, S. Pourshahabi, and M. Sadegh. 2018. “Optimal joint deployment of flow and pressure sensors for leak identification in water distribution networks.” Urban Water J. 15 (9): 837–846. https://doi.org/10.1080/1573062X.2018.1561915.
Saltelli, A. 2002. “Making best use of model evaluations to compute sensitivity indices.” Comput. Phys. Commun. 145 (2): 280–297. https://doi.org/10.1016/S0010-4655(02)00280-1.
Santos-Ruiz, I., F. R. López-Estrada, V. Puig, G. Valencia-Palomo, and H. R. Hernández. 2022. “Pressure sensor placement for leak localization in water distribution networks using information theory.” Sensors 22 (2): 443. https://doi.org/10.3390/s22020443.
Shahapure, K. R., and C. Nicholas. 2020. “Cluster quality analysis using silhouette score.” In Proc., 7th Int. Conf. on Data Science and Advanced Analytics, 747–748. New York: IEEE. https://doi 10.1109/DSAA49011.2020.00096.
Soldevila, A., J. Blesa, S. Tornil-Sin, R. M. Fernandez-Canti, and V. Puig. 2018. “Sensor placement for classifier-based leak localization in water distribution networks using hybrid feature selection.” Comput. Chem. Eng. 108 (Aug): 152–162. https://doi.org/10.1016/j.compchemeng.2017.09.002.
Song, X., J. Zhang, C. Zhan, Y. Xuan, M. Ye, and C. Xu. 2015. “Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications.” J. Hydrol. 523 (Feb): 739–757. https://doi.org/10.1016/j.jhydrol.2015.02.013.
Soroush, F., and M. J. Abedini. 2019. “Optimal selection of number and location of pressure sensors in water distribution systems using geostatistical tools coupled with genetic algorithm.” J. Hydroinf. 21 (6): 1030–1047. https://doi.org/10.2166/hydro.2019.023.
Steffelbauer, D. B., J. Deuerlein, D. Gilbert, E. Abraham, and O. Piller. 2022. “Pressure-leak duality for leak detection and localization in water distribution systems.” J. Water Resour. Plann. Manage. 148 (3): 04021106. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001515.
Taravatrooy, N., M. R. Nikoo, S. Hobbi, M. Sadegh, and A. Izady. 2020. “A novel hybrid entropy-clustering approach for optimal placement of pressure sensors for leakage detection in water distribution systems under uncertainty.” Urban Water J. 17 (3): 185–198. https://doi.org/10.1080/1573062X.2020.1758162.
Walski, T. M., et al. 1987. “Battle of the network models: Epilogue.” J. Water Resour. Plann. Manage. 113 (2): 191–203. https://doi.org/10.1061/(ASCE)0733-9496(1987)113:2(191).
Wéber, R., and C. Hős. 2020. “Efficient technique for pipe roughness calibration and sensor placement for water distribution systems.” J. Water Resour. Plann. Manage. 146 (1): 04019070. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001150.
Wu, Z. Y., and T. M. Walski. 2012. “Effective approach for solving battle of water calibration network problem.” J. Water Resour. Plann. Manage. 138 (5): 533–542. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000193.
Xie, X., H. Zhang, and D. Hou. 2017. “Bayesian approach for joint estimation of demand and roughness in water distribution systems.” J. Water Resour. Plann. Manage. 143 (8): 04017034. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000791.
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Published In
Journal of Water Resources Planning and Management
Volume 150 • Issue 5 • May 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: May 25, 2023
Accepted: Dec 10, 2023
Published online: Feb 27, 2024
Published in print: May 1, 2024
Discussion open until: Jul 27, 2024
ASCE Technical Topics:
- Analysis (by type)
- Calibration
- Engineering fundamentals
- Field tests
- Mathematical functions
- Mathematics
- Matrix (mathematics)
- Measurement (by type)
- Network analysis
- Sensitivity analysis
- Sensors and sensing
- Tests (by type)
- Water and water resources
- Water leakage and water loss
- Water management
- Water supply
- Water supply systems
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