Calculation of Leakage in Water Supply Network Based on Blind Source Separation Theory

Document Type: Research Paper


Department of Municipal Engineering, Harbin Institute of Technology, Haerbin Shi, Heilongjiang Sheng, China.


The economic and environmental losses due to serious leakage in the urban water supply network have increased the effort to control the water leakage. However, current methods for leakage estimation are inaccurate leading to the development of ineffective leakage controls. Therefore, this study proposes a method based on the blind source separation theory (BSS) to calculate the leakage of water supply network. The method uses fast independent component analysis (FastICA) algorithm to separate flow signal of laboratory and practical measuring area, adopts trend similarity to solve the uncertainty of separation sequence to get hourly change curve of user usage and physical leakage, and embeds the leakage model into amplitude optimization model to solve amplitude uncertainty to obtain physical leakage value. The study found that the estimation of leakage level using the blind source separation is reasonably accurate and facilitates the identification of the subsequent reduction in water leakage. This can provide scientific evidence for leakage reduction and the investment of pressure relief devices in the next stage.


  1. Almandoz, J., Cabrera, E., Arregui, F., et al. 2005 Leakage assessment through water distribution network simulation. Journal of water resources planning and management. 131(6), 458-466.
  2. American Water Works Association , 1999 2nd edition, Manual M36, Water audits and leak detection, AWWA.
  3. Buchberger, S. & Nadimpali, G. 2004 Leak estimation in water distribution system by statistical analysis of flow readings. J. Water Resour. Plann. and Manage. 130(4), 321-329.
  4. Comon, P. 1994 Independent component analysis, a new concept? J. Signal Processing. 36(3), 287-314.
  5. Comon, P. & Jutten, C. (Eds.). 2010 Handbook of blind source separation: Independent component analysis and applications. Academic press.
  6. Gao, J., Qi, S. & Wu, W. 2014 Study on leakage rate in water distribution network using fast independent component analysis. WDSA 2014, Bari, Italy: Procedia Engineering. 89(2014), 934-941.
  7. Hyvärinen, A. 1999 Fast and robust fixed-point algorithms for independent component analysis. J. Neural Networks. 10(3), 626-634.
  8. Hyvärinen, A. & Oja, E. 2000 Independent component analysis: algorithms and applications. J. Neural Networks. 13(4-5), 411-430.
  9. Mathworks. 2014 < > (Accessed 04 May 2015).
  10. Rossman, L. 2000 EPANET User's Manual. United States Environmental Protection Agency, Cincinnati, U.S.A.
  11. Tabesh, M., Tanyimboh, T.T. & Burrows, R. 2002 Head driven simulation of water supply networks. IJE Transactions A: Basics. 15(1), 11-22.
  12. Tabesh, M., Asadiyani, A.H. & Burrows, R. 2009 An Integrated Model to Evaluate Losses in Water Distribution Systems. Water Resources Management. 23(3), 477-492.
  13. Tong, L., Liu, R. & Soon, V.C. 1991 Inderminacy and identifiability of blind identification. IEEE Transactions on Circuits and Systems. 38(5), 499-509.
  14. Winarni, W. 2009 Infrastructure Leakage Index (ILI) as water losses indicator. Civil Engineering Dimension. 11(2), 126-134.