Abstract
Water Network Partitioning (WNP) is among the most attractive and studied strategies for the improvement of the Water Distribution Network (WDN) management. The proper definition of sub-regions (called clusters or districts) with high link density between nodes in the same group, and a relatively low link density between nodes in different groups, is a crucial aspect for the partitioning of a water system. If on one hand the definition of these monitored sub-areas, called District Metered Areas (DMAs), allows simplifying the water balance, the pressure control, the water leaks identification and the water quality protection, on the other hand it may worsen the hydraulic performance and the reliability of the system. In this paper, two clustering algorithms, graph partitioning based on a Multi-Level Recursive Bisection and Spectral Clustering, were used to define the districts. Some of the major geometrical and hydraulic characteristics of the network has been adopted as weights in the partitioning procedure. A comparison between the two clustering methods was made for a real water network of the South Italy, Parete, evaluating some clustering quality and hydraulic indices, in order to define the algorithm and the weight which work better for the definition of the optimal clustering layout.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gunter, S., Bunke, H.: Validation indices for graph clustering. Pattern Recogn. Lett. Graph-based Represent. Pattern Recogn. 24, 1107–1113 (2003)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)
Schaeffer, S.E.: Survey: graph clustering. Comput. Sci. Rev. 1, 27–64 (2007)
Dubes, R.: Cluster analysis and related issues. In: Chen, C., Pau, L., Wang, P. (eds.), Handbook of Pattern Recognition and Computer Vision, pp. 3–32. World Scientific Publishing Co. Inc. River Edge, NJ, USA, (1993)
Huttenhower, C., Flamholz, A.I., Landis, J.N., Sahi, S., Myers, C.L., Olszewski, K.L., Hibbs, M.A., Siemers, N.O., Troyanskaya, O.G., Coller, H.A.: Nearest neighbor networks: clustering expression data based on gene neighborhoods. BMC Bioinform. 12, 8–250 (2007)
Ushioda, A., Kawasaki, J.: Hierarchical clustering of words and application to nlp tasks. In: Ejerhed, E., Dagan, I. (eds.) Fourth Workshop on Very Large Corpora, pp. 28–41. Association for Computational Linguistics, Somerset, New Jersey (1996)
White, S.D.M., Frenk, C.S.: Galaxy formation through hierarchical clustering. Astrophys. J. 379, 52–79 (1991)
Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 15, 1101–1113 (1993)
Guimerà, R., Mossa, S., Turtschi, A., NunesAmaral, L.A.: The worldwide air transportation network: anomalous centrality, community structure, and cities’ global roles. Proc. Natl. Acad. Sci. USA 102, 7794–7799 (2005)
Bar-Ilan, J., Kortsarz, G., Peleg, D.: How to allocate network centers. J. Algorithm. 15, 385–415 (1993)
Di Nardo, A., Di Natale, M., Santonastaso, G.F., Tzatchkov, V.G., Alcocer Yamanaka, V.H.: Divide and conquer partitioning techniques for smart water networks. Proc. Eng. 89, 1176–1183 (2014)
Di Nardo, A., Di Natale, M., Giudicianni, C., Musmarra, D., Santonastaso, G.F., Simone, A.: Water distribution system clustering and partitioning based on social network algorithms. Proc. Eng. 119, 196–205 (2015)
Nascimento, M.C.V., de Carvalho, A.C.P.L.F.: Spectral methods for graph clustering-A survey. Eur. J. Oper. Res. 211, 221–231 (2011)
Di Nardo, A., Di Natale, M., Giudicianni, C., Greco, R., Santonastaso, G.F.: Water supply network partitioning based on weighted spectral clustering. Stud. Comput. Intell. Complex Netw. Appl. 693, 797–807 (2016)
Boccaletti, S., Latora, V., Morenod, Y., Chavezf, M., Hwanga, D.-U.: Complex networks Structure and dynamics. Phys. Rep. 424, 175–308 (2006)
Mays, W.: Water Distribution Systems Handbook. McGraw-Hill, New York (2000)
Di Nardo A., Di Natale M., Giudicianni C., Greco R., Santonastaso G.F.: Complex network and fractal theory for the assessment of water distribution network resilience to pipe failures. Water Sci. Technol. Water Suppl (2017). https://doi.org/10.2166/ws.2017.124
Alonso, J.M., Alvarruiz, F., Guerrero, D., Hernàndez, V., Ruiz, P.A., Vidal, A.M., Martìnez, F., Vercher, J., Ulanicki, B.: Parallel computing in water network analysis and leakage minimization. J. Hydr. Eng. ASCE 126, 251–260 (2000)
Water Industry Research Ltd: A Manual of DMA Practice. UK Water Industry Research, London (1999)
Di Nardo, A., DiNatale, M., Musmarra, D., Santonastaso, G.F., Tzatchkov, V., Alcocer-Yamanaka, V.H.: Dual-use value of network partitioning for water system management and protection from malicious contamination. J. Hydroinform. 17, 361–376 (2015a)
Campbell, E., Izquierdo, J., Montalvo, I., Pérez-García, R.: A novel water supply network sectorization methodology based on a complete economic analysis, including uncertainties. Water 8, 179 (2016)
Di Nardo, A., Di Natale, M., Santonastaso, G.F., Venticinque, S.: An automated tool for smart water network partitioning. Water Resour. Manage 27, 4493–4508 (2013a)
Perelman, L.S., Allen, M., Preis, A., Iqbal, M., Whittle, A.J.: Automated sub-zoning of water distribution systems. Environ. Model Softw. 65, 1–14 (2015)
Di Nardo, A., Di Natale, M., Santonastaso, G.F., Tzatchkov, V.G., Alcocer Yamanaka, V.H.: Water network sectorization based on genetic algorithm and minimum dissipated power paths. J. Water Sci. Technol. Water Supply 13, 951–957 (2013b)
Karypis, G., Kumar, V.: Multilevel k-way partitioning scheme for irregular graphs. J. Parallel Distrib. Comput. 48, 96–129 (1998)
Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20, 359–392 (1999)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 888–905 (2000)
Di Nardo, A., Di Natale, M., Greco, R., Santonastaso, G.F.: Antalgorithm for smart water network partitioning. Proc. Eng. 70, 525–534 (2014)
Von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17, 395–416 (2007)
Di Nardo, A., Di Natale, M., Giudicianni, C., Greco, R., Santonastaso, G.F.: Weighted spectral clustering for water distribution network partitioning. Appl. Netw. Sci. 2, 19 (2017a)
Fiedler, M.: Algebraic connectivity of graphs. Czech. Math. J. 23, 298 (1973)
Yazdani, A., Jeffrey, P.: Complex network analysis of water distribution systems. Interdisc. J. Nonlinear Sci. Chaos 21, 016111 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Di Nardo, A., Di Natale, M., Giudicianni, C., Greco, R., Santonastaso, G. (2018). Water Distribution Network Clustering: Graph Partitioning or Spectral Algorithms?. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_97
Download citation
DOI: https://doi.org/10.1007/978-3-319-72150-7_97
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72149-1
Online ISBN: 978-3-319-72150-7
eBook Packages: EngineeringEngineering (R0)