A Novel Approach for Leak Localization in Water Distribution Networks Using Computational Intelligence

  • Maibeth Sánchez-RiveroEmail author
  • Marcos Quiñones-Grueiro
  • Alejandro Rosete Suárez
  • Orestes Llanes Santiago
Part of the Studies in Computational Intelligence book series (SCI, volume 872)


This article provides a new approach to localize leaks in water distribution networks (WDN). The model-based leak localization is formulated as an inverse problem solved by using optimization tools. In most approaches to leak localization currently described in the scientific literature, the performance depends on the labeling method for nodes in the parameter space of objective functions. The main contribution of this article lies in a new model-based leak localization approach for water distribution networks whose performance does not depends on the node labeling method. As a result, the accuracy and efficiency of the optimization tools used for leak location task are improved. In addition, the proposal does not depend on the sensitivity matrix used in other methods to localize leaks. In order to achieve all the above, the method hereby proposed builds upon a localization strategy based on the network topological configuration. The Hanoi WDN is used to validate the proposed methodology where the metaheuristic algorithms differential evolution, particle swarm optimization, and simulated annealing are used as optimization tools.


Leak detection and localization Water distribution networks Optimization algorithm Computational intelligence Inverse problems 


  1. 1.
    UN: World urbanization prospects: the 2007 revision population database (2008).
  2. 2.
    Puust, R., Kapelan, Z.S., Savic, D., Koopel, T.: J. Urban Water 7(1), 25 (2010). CrossRefGoogle Scholar
  3. 3.
    Loveday, M., Dixon, J.: Proceedings IWA Leakage Conference, Halifax (2005)Google Scholar
  4. 4.
    MacDonald, G., Yates, C.: Proceedings IWA Leakage Conference, Halifax (2005)Google Scholar
  5. 5.
    Covas, D., Ramos, H., de Almeida, A.B.: J. Hydraul. Eng. 131(12), 1106 (2005)CrossRefGoogle Scholar
  6. 6.
    Muggleton, J.M., Brennan, M.J.: J. Sound Vib. 278, 527 (2004)CrossRefGoogle Scholar
  7. 7.
    Farley, M., Trow, S.: Losses in Water Distribution Networks A Practitioner’s Guide to Assessment, Monitoring and Control. IWA Publishing, London (2003)Google Scholar
  8. 8.
    Mergelas, B., Henrich, G.: Proceedings of the Leakage 2005 Conference, Halifax (2005)Google Scholar
  9. 9.
    Farah, E., Shahrour, I.: Water Res. Manag. 31(15), 4821 (2017)CrossRefGoogle Scholar
  10. 10.
    Pérez, R., Puig, V., Pascual, J., Quevedo, J., Landeros, E., Peralta, A.: Control Eng. Pract. 19(10), 1157 (2011). CrossRefGoogle Scholar
  11. 11.
    Pérez, R., Puig, V., Peralta, A., Landeros, E., Jordanas, L.: Water Sci. Technol. Water Supply 9(6), 715 (2009). CrossRefGoogle Scholar
  12. 12.
    Pudar, R.S., Liggett, J.A.: J. Hydraul. Eng. 118(7), 1031 (1992)CrossRefGoogle Scholar
  13. 13.
    Steffelbauer, D.B., Günther, M., Fuchs-Hanusch, D.: Proc. Eng. 186, 444 (2017). CrossRefGoogle Scholar
  14. 14.
    Storn, R., Price, K.: Technical Report TR-95-012, International Computer Science Institute, University of California (1995)Google Scholar
  15. 15.
    Kennedy, J., Eberhart, R.: IEEE International Conference on Neural Networks, Perth, pp. 1942–1948 (1995)Google Scholar
  16. 16.
    Cerny, V.: J. Optim. Theory Appl. 45(l), 41 (1985)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Science 220(4598), 671 (1983)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Camps Echevarría, L., Llanes-Santiago, O., da Silva Neto, A.J.: In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010 (2010), September 2015.
  19. 19.
    Datta, D., Rui, J.: Appl. Soft Comput. J. 13(9), 3884 (2013). CrossRefGoogle Scholar
  20. 20.
    Casillas Ponce, M.V., Castañón, L.E.G., Puig, V.: J. Hydroinf. 16(3), 649 (2014). CrossRefGoogle Scholar
  21. 21.
    Fujiwara, O., Khang, D.B.: Water Resour. Res. 26(4), 539 (1990)CrossRefGoogle Scholar
  22. 22.
    Tospornsampan, J., Kita, I., Ishii, M., Kitamura, Y.: Int. J. Comput. Inf. Syst. Sci. 1(4), 28 (2007)Google Scholar
  23. 23.
    Rossman, L.A.: Water supply and water resources division. National Risk Management Research Laboratory. EPANET 2 User’s Manual. Technical Report. September (2000).
  24. 24.
    Talbi, E.G.: Metaheuristics from Design to Implementation (Wiley, London, 2009)zbMATHGoogle Scholar
  25. 25.
    Soldevila, A., Blesa, J., Tornil-Sin, S., Duviella, E., Fernandez-canti, R.M., Puig, V.: Control Eng. Pract. 55, 162 (2016). CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Automation and Computing DepartmentUniversidad Tecnológica de La Habana José Antonio Echeverría, CUJAELa HabanaCuba
  2. 2.Artificial Intelligence DepartmentUniversidad Tecnológica de La Habana José Antonio Echeverría, CUJAELa HabanaCuba

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