Identifying an Appropriate Analysis Duration for the Principal Component Analysis of Water Pipe Flow Data

상수도 관망 유량관측 자료의 주성분 분석을 위한 분석기간의 설정

  • Received : 2012.12.16
  • Accepted : 2013.06.12
  • Published : 2013.06.15


In this study the Principal Component Analysis (PCA) was applied to flow data in a water distribution pipe system to analyze the relevance between the flow observation dates, which have the outliers of observed night flows, and the maintenance records. The data was obtained from four small size water distribution blocks to which 13 maintenance records such as pipe leak and water meter leak belong. The flow data during four months were used for the analysis. The analysis was carried out to identify an appropriate analysis period for a PCA model for a water distribution block. To facilitate the analyses a computational algorithm was developed. MATLAB was utilized to realize the algorithm as a computer program. As a result, an appropriate PCA period for each of the case study small size water distribution blocks was identified.


analysis period;computational algorithm;night flows;Principal Component Analysis;water distribution pipe system


  1. Covas, D. Ramos, H. Lopes, N. and Almeida, A.B. (2006) Water pipe system diagnosis by transient pressure signals, In: Proceedings of the 8th Annual Water Distribution Systems Analysis Symposium. Cincinnati, USA.
  2. Ministry of Environment (2010) Water Suppy Statistics
  3. Borges, L.A. and Ramirez, M.A. (2010) Acoustic Water Leak Detection System, In: Proceedings of the 7th International Telecommunications Symposium, pp. 1-3.
  4. Bougadis, J. Adamowski, K. and Diduch, R. (2005) Shortterm municipal water demand forecasting, Hydrological Processing, 19(1), 137-148.
  5. Farley, M. and Trow, S. (2003) Losses in water distribution networks, London: IWA Publishing.
  6. Kapelan, Z. Savic, D.A. and Walters, G.A. (2004) Incorporation of prior information on parameters in inverse transient analysis for leak detection and roughness calibration, Urban Water Journal, 1(2), pp.129-143.
  7. Lambert, A. and Hirner, W.H. (2000) Losses from Water Supply Systems: Standard Terminology and Performance Measures, IWSA Blue Pages.
  8. Mergelas, B. and Henrich, G. (2005) Leak locating method for precommissioned transmission pipelines: North American case studies, In: Leakage 2005 Conference Proceedings. Halifax, Canada.
  9. Mounce, S.R. Boxall, J. and Machell, J. (2009) Development and verification of an online artificial intelligence system for detection of bursts and other abnormal flows, Journal of Water Resources Planning and Management, 136(3), May/June 2010, pp. 309-318.
  10. Muggleton, J.M. Brennan, M.J. Pinnington, R.J. and Gao, Y. (2006) A novel sensor for measuring the acoustic pressure in buried plastic water pipes, Journal of Sound and Vibration, 295(3-5), pp.1085-1098.
  11. Mounce, S.R. Day, A.J. Wood, A.S. and Khan, A. (2002) A neural network approach to burst detection, Water science and technology, 45(4-5), pp. 237-246.
  12. Muggleton, J.M. and Brennan, M.J. (2005) Axisymmetric wave propagation in buried, fluid-filled pipes: effects of wall discontinuities, Journal of Sound and Vibration, 281(3-5), pp.849-867.
  13. O'Brien, E. Murray, T. and McDonald, A. (2003) Detecting leaks from water pipes at a test facility using ground penetrating radar, In: Proceedings of PEDS 2003 (Pumps, Electromechanical Devices and Systems Applied to Urban Water Management), Valencia, Spain.
  14. Palau, C.V. Arregui, F. and Ferrer, A. (2004) Using multivariate principal component analysis of injected water flows to detect anomalous behaviors in a water supply system. a case study, Water Supply, IWA, 4(3), pp. 169-181.
  15. Pilcher, R. Hamilton, S. Chapman, H. Ristovski, B. and Strapely, S. (2007) Leak location and repair guidance notes, In: International Water Association, Water Loss Task Forces: Specialist Group Efficient Operation and Management, Bucharest, Romania.
  16. Stathis, J.A. and Loganathan, G.V. (1999) Analysis of pressure-dependent leakage in water distribution systems, Analysis of pressure-dependent leakage in water distribution systems, In: Preparing for the 21st Century, 29th Annual Water Resources Planning and Management Conference, Tempe, AZ, USA.
  17. Tabesh, M. and Delavar, M.R. (2003) Application of integrated GIS and hydraulic models for unaccounted for water studies in water distribution systems, In: Proceedings of the International Conference on Advances in Water Supply Management. London: Balkema.
  18. Tajima, M. and Mita, A. (2009) Automatic Leakage Detection for Water Supply Systems Using Principal Component Analysis, MATERIALS FORUM-PARKVILLE-CD ROM EDITION-, 33, pp. 10.
  19. Xia, L. and Guo-jin, L. (2010) Leak Detection of Municipal Water Supply Network Based on the Cluster-analysis and Fuzzy Pattern Recognition, International Conference on E-Product, E-Service, and EEntertainment(ICEEE).
  20. Ye, G and Fenner, Ra. (2011) Kalman fifiltering of hydraulic measurements for burst detection in water distribution systems, Journal of Pipeline Systems Engineering and Practice (ASCE), 2. pp. 14-22. ISSN 1949-1190.

Cited by

  1. Determining the Time of Least Water Use for the Major Water Usage Types in District Metered Areas vol.29, pp.3, 2015,


Supported by : 부산대학교