Effective Local Metric Learning for Water Pipe Assessment

  • Mojgan Ghanavati
  • Raymond K. WongEmail author
  • Fang Chen
  • Yang Wang
  • Simon Fong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9651)


Australia’s critical water pipes break on average 7, 000 times per year. Being able to accurately identify which pipes are at risk of failure will potentially save Australia’s water utilities and the community up to \( \$700 \) million a year in reactive repairs and maintenance. However, ranking these water pipes according to their calculated risk has mixed results due to their different types of attributes, data incompleteness and data imbalance. This paper describes our experience in improving the performance of classifying and ranking these data via local metric learning. Distance metric learning is a powerful tool that can improve the performance of similarity based classifications. In general, global metric learning techniques do not consider local data distributions, and hence do not perform well on complex / heterogeneous data. Local metric learning methods address this problem but are usually expensive in runtime and memory. This paper proposes a fuzzy-based local metric learning approach that out-performs recently proposed local metric methods, while still being faster than popular global metric learning methods in most cases. Extensive experiments on Australia water pipe datasets demonstrate the effectiveness and performance of our proposed approach.


Learning Method Artificial Neural Network Model Friedman Test Membership Degree Water Pipe 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Bellet, A., Habrard, A., Sebban, M.: A survey on metric learning for feature vectors and structured data. arXiv preprint arXiv:1306.6709 (2013)
  2. 2.
    Bohné, J., Ying, Y., Gentric, S., Pontil, M.: Large margin local metric learning. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision – ECCV 2014, Part II. LNCS, vol. 8690, pp. 679–694. Springer, Heidelberg (2014)Google Scholar
  3. 3.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar
  4. 4.
    He, Y., Chen, W., Chen, Y., Mao, Y.: Kernel density metric learning. In: IEEE 13th International Conference on Data Mining (ICDM), 2013. pp. 271–280. IEEE (2013)Google Scholar
  5. 5.
    Huang, H.C., Chuang, Y.Y., Chen, C.S.: Multiple kernel fuzzy clustering. IEEE Trans. Fuzzy Syst. 20(1), 120–134 (2012)CrossRefGoogle Scholar
  6. 6.
    Ibrahim, J.G., Chen, M.H., Sinha, D.: Bayesian survival analysis. Wiley Online Library, New York (2005)CrossRefzbMATHGoogle Scholar
  7. 7.
    Jafar, R., Shahrour, I., Juran, I.: Application of artificial neural networks (ANN) to model the failure of urban water mains. Math. Comput. Model. 51(9), 1170–1180 (2010)CrossRefGoogle Scholar
  8. 8.
    Kleiner, Y., Rajani, B.: Comparison of four models to rank failure likelihood of individual pipes. J. Hydroinformatics 14(3), 659–681 (2012)CrossRefGoogle Scholar
  9. 9.
    Li, Z., Zhang, B., Wang, Y., Chen, F., Taib, R., Whiffin, V., Wang, Y.: Water pipe condition assessment: a hierarchical beta process approach for sparse incident data. Mach. Learn. 95(1), 11–26 (2014)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Liu, T., Moore, A.W., Yang, K., Gray, A.G.: An investigation of practical approximate nearest neighbor algorithms. In: Advances in neural information processing systems. pp. 825–832 (2004)Google Scholar
  11. 11.
    Liu, W., Tsang, I.W.: Large margin metric learning for multi-label prediction. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)Google Scholar
  12. 12.
    Megano, T., Fukui, K.i., Numao, M., Ono, S.: Evolutionary multi-objective distance metric learning for multi-label clustering. In: 2015 IEEE Congress on Evolutionary Computation (CEC). pp. 2945–2952. IEEE (2015)Google Scholar
  13. 13.
    Noh, Y.K., Zhang, B.T., Lee, D.D.: Generative local metric learning for nearest neighbor classification. In: NIPS. pp. 1822–1830 (2010)Google Scholar
  14. 14.
    Tabesh, M., Soltani, J., Farmani, R., Savic, D.: Assessing pipe failure rate and mechanical reliability of water distribution networks using data-driven modeling. J. Hydroinformatics 11(1), 1–17 (2009)CrossRefGoogle Scholar
  15. 15.
    Wan, S., Aggarwal, J.: Spontaneous facial expression recognition: a robust metric learning approach. Pattern Recogn. 47(5), 1859–1868 (2014)CrossRefGoogle Scholar
  16. 16.
    Wang, J., Kalousis, A., Woznica, A.: Parametric local metric learning for nearest neighbor classification. In: NIPS. pp. 1610–1618 (2012)Google Scholar
  17. 17.
    Wang, Y., Zayed, T., Moselhi, O.: Prediction models for annual break rates of water mains. J. Perform. Constructed Facil. 23(1), 47–54 (2009)CrossRefGoogle Scholar
  18. 18.
    Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10, 207–244 (2009)zbMATHGoogle Scholar
  19. 19.
    Wu, L., Jin, R., Hoi, S.C., Zhu, J., Yu, N.: Learning Bregman distance functions and its application for semi-supervised clustering. In: NIPS. pp. 2089–2097 (2009)Google Scholar
  20. 20.
    Xiong, C., Johnson, D., Xu, R., Corso, J.J.: Random forests for metric learning with implicit pairwise position dependence. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 958–966. ACM (2012)Google Scholar
  21. 21.
    Xu, Q., Chen, Q., Ma, J., Blanckaert, K.: Optimal pipe replacement strategy based on break rate prediction through genetic programming for water distribution network. J. Hydro-Environ. Res. 7(2), 134–140 (2013)CrossRefGoogle Scholar
  22. 22.
    Yu, J., Tao, D., Li, J., Cheng, J.: Semantic preserving distance metric learning and applications. Inf. Sci. 281, 674–686 (2014)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mojgan Ghanavati
    • 1
  • Raymond K. Wong
    • 1
    Email author
  • Fang Chen
    • 2
  • Yang Wang
    • 2
  • Simon Fong
    • 3
  1. 1.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia
  2. 2.National ICT Australia (NICTA)SydneyAustralia
  3. 3.University of MacauMacauChina

Personalised recommendations