Weighted Least Squares with Expectation-Maximization Algorithm for Burst Detection in U.K. Water Distribution Systems
Publication: Journal of Water Resources Planning and Management
Volume 140, Issue 4
Abstract
Flow measurement data at the district meter area (DMA) level has the potential for burst detection in the water distribution systems. This work investigates using a polynomial function fitted to the historic flow measurements based on a weighted least-squares method for automatic burst detection in the U.K. water distribution networks. This approach, when used in conjunction with an expectation-maximization (EM) algorithm, can automatically select useful data from the historic flow measurements, which may contain normal and abnormal operating conditions in the distribution network, e.g., water burst. Thus, the model can estimate the normal water flow (nonburst condition), and hence the burst size on the water distribution system can be calculated from the difference between the measured flow and the estimated flow. The distinguishing feature of this method is that the burst detection is fully unsupervised, and the burst events that have occurred in the historic data do not affect the procedure and bias the burst detection algorithm. Experimental validation of the method has been carried out using a series of flushing events that simulate burst conditions to confirm that the simulated burst sizes are capable of being estimated correctly. This method was also applied to eight DMAs with known real burst events, and the results of burst detections are shown to relate to the water company’s records of pipeline reparation work.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors would like to thank Mr. Ridwan Patel in Yorkshire Water Services and Professor Joby Boxall, Dr. John Machell, and Dr. Steve Mounce at the University of Sheffield for conducting fieldwork and assistance with data sets. The authors also acknowledge funding from EPSRC, which supported this work (Grant No. EP/E003192/1).
References
Andersen, J. H., and Powell, R. S. (2000). “Implicit state-estimation technique for water network monitoring.” Urban Water, 2(2), 123–130.
Bishop, C. M. (1995). Neural networks for pattern recognition, Oxford University Press, Oxford, U.K.
Buchberger, S. G., and Nadimpalli, G. (2004). “Leak estimation in water distribution systems by statistical analysis of flow readings.” J. Water Resour. Plann. Manage., 321–329.
Colombo, A. F., and Karney, B. W. (2002). “Energy and costs of leaky pipes: Toward comprehensive picture.” J. Water Resour. Plann. Manage., 441–450.
Colombo, F. A., Lee, P., and Karney, B. W. (2009). “A selective literature review of transient-based leak detection methods.” J. Hydro-environ. Res., 2(4), 212–227.
Donoho, D. L., and Johnstone, I. M. (1995). “Adapting to unknown smoothness via wavelet shrinkage.” J. Am. Stat. Assoc., 90(432), 1200–1224.
Farley, B., Boxall, J. B., and Mounce, S. R. (2008). “Optimal locations of pressure meter for burst detection.” 10th Water Distribution System Analysis Symp., ASCE, Reston, VA.
Forsyth, D. A., and Ponce, J. (2002). Computer vision: A modern approach, Prentice Hall, Upper Saddle River, NJ.
Karim, M. R., Abbaszadegan, M., and Lechevallier, M. (2003). “Potential for pathogen intrusion during pressure transients.” J. Am. Water Works Assoc., 95(5), 134–146.
Khan, A., Widdop, P. D., Day, A. J., Wood, A. S., Mounce, S. R., and Machell, J. (2005). “Artificial neural network model for a low cost failure sensor: Performance assessment in pipeline distribution.” Trans. Eng. Comput. Technol., 15.
Kumar, S. M., Narasimhan, S., and Bhallamudi, S. M. (2008). “State estimation in water distribution networks using graph-theoretic reduction strategy.” J. Water Resour. Plann. Manage., 395–403.
Liggett, J. A., and Chen, L.-C. (1994). “Inverse transient analysis in pipe networks.” J. Hydraul. Eng., 934–955.
Mounce, S. R., Boxall, J. B., and Machell, J. (2007). “An artificial neural network/fuzzy logic system for DMA flow meter data analysis providing burst identification and size estimation.” Water management challenges in global change, B. Ulanicki, D. Butler, F. A. Memon, K. Vairavamoorthy, and P. L. M. Bounds, eds., Taylor & Francis, London, 313–320.
Mounce, S. R., Boxall, J. B., and Machell, J. (2010). “Development and verification of an online artificial intelligence system for burst detection in water distribution systems.” J. Water Resour. Plann. Manage., 309–318.
Mounce, S. R., Day, A. J., Wood, A. S., Khan, A., Widdop, P. D., and Machell, J. (2002). “A neural network approach to burst detection.” Water Sci. Technol., 45(4–5), 237–246.
Mounce, S. R., Khan, A., Wood, A. S., Day, A. J., Widdop, P. D., and Machell, J. (2003). “Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system.” Inform. Fusion, 4(3), 217–229.
Mounce, S. R., and Machell, J. (2006). “Burst detection using hydraulic data from water distribution systems with artificial neural networks.” Urban Water, 3(1), 21–31.
Obradovic, D. (2000). “Modelling of demand and losses in real-life water distribution systems.” Urban Water, 2(2), 131–139.
Palau, C. V., Arregui, F. J., and Carlos, M. (2012). “Burst detection in water networks using principal component analysis.” J. Water Resour. Plann. Manage., 47–54.
Poulakis, Z., Valougeorgis, D., and Papadimitriou, C. (2003). “Leakage detection in water pipe networks using a Bayesian probabilistic framework.” Probab. Eng. Mech., 18(4), 315–327.
Quevedoa, J., Puiga, V., Cembranoa, G., and Blancha, J. (2010). “Validation and reconstruction of flow meter data in the Barcelona water distribution network.” Control Eng. Pract., 18(6), 640–651.
Romano, M., Kapelan, Z., and Savic, D. (2010). “Pressure signal de-noising for improved realtime leak detection.” 9th Int. Conf. on Hydroinformatics, International Water Association, London, U.K.
Romano, M., Kapelan, Z., and Savic, D. (2012). “Automated detection of pipe bursts and other events in water distribution systems.” J. Water Resour. Plann. Manage.,.
Wu, Z. Y. (2008). “Data usage protocol for leakage detection and EPS model calibration.” Water distribution systems analysis, ASCE, Reston, VA.
Wu, Z. Y., Sage, P., Turtle, D., and Wheeler, M. (2008). “Leak detection case study by means of optimizing emitter locations and flows.” Water distribution systems analysis, ASCE, Reston, VA.
Ye, G. (2008). “Model-based ultrasonic temperature estimation for monitoring HIFU therapy.” D.Phil. thesis, University of Oxford, Oxford, U.K.
Ye, G., and Fenner, R. A. (2011). “Kalman filtering of hydraulic measurements for burst detection in water distribution systems.” J. Pipeline Syst. Eng. Pract., 14–22.
Ye, G., Noble, J. A., and Probert Smith, P. (2008). “A model-based displacement outlier removal algorithm for ultrasonic temperature estimation.” IEEE UFFC Int. Ultrasonics Symp, IEEE-UFFC Society, Champaign, IL.
Yurdusev, M. A., Firat, M., Mermer, M., and Turan, M. E. (2009). “Water use prediction by radial and feed-forward neural nets.” Water Manage., 162(3), 179–188.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 140 • Issue 4 • April 2014
Pages: 417 - 424
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Jun 4, 2012
Accepted: Jan 3, 2013
Published online: Jan 5, 2013
Discussion open until: Jun 5, 2013
Published in print: Apr 1, 2014
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.