Development and Verification of an Online Artificial Intelligence System for Detection of Bursts and Other Abnormal Flows
Publication: Journal of Water Resources Planning and Management
Volume 136, Issue 3
Abstract
Water lost through leakage from water distribution networks is often appreciable. As pressure increases on water resources, there is a growing emphasis for water service providers to minimize this loss. The objective of the work presented in this paper was to assess the online application and resulting benefits of an artificial intelligence system for detection of leaks/bursts at district meter area (DMA) level. An artificial neural network model, a mixture density network, was trained using a continually updated historic database that constructed a probability density model of the future flow profile. A fuzzy inference system was used for classification; it compared latest observed flow values with predicted flows over time windows such that in the event of abnormal flow conditions alerts are generated. From the probability density functions of predicted flows, the fuzzy inference system provides confidence intervals associated with each detection, these confidence values provide useful information for filtering and ranking alerts. Additionally an accurate estimate of abnormal flow magnitude is produced to further aid in ranking of alerts. A water supply system in the U.K. was used for a case study with near real-time flow data provided by general packet radio service. The online burst alert system was constructed to operate alongside an existing flat-line alarm system, and continuously analyze a set of 144 DMAs every hour. The new system identified a number of events and alerts were raised prior to their detection in the control room; either through flat-line alarms or customer contacts. Examples are given of alert correlation with burst reports and subsequent mains repairs for a 2-month trial period. Forty four percent of alerts were found to correspond to bursts confirmed by repair data or customer contacts, 32% of alerts were confirmed as unusual short-term demand from manual analysis, 9% were related to known industrial events, and only 15% were ghosts. The results indicate that the system is an effective and viable tool for online burst detection in water distribution systems with the potential to save water and improve customer service.
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Acknowledgments
The writers wish to acknowledge the support given for this research by Yorkshire Water Services Ltd., U.K., and the time, energy and enthusiasm of the specific individuals involved.
References
Ackoff, R. L. (1989). “From data to wisdom.” Journal of Applied Systems Analysis, 16, 3–9.
Anmala, J., Zhang, B., and Govindaraju, R. (2000). “Comparison of ANNs and empirical approaches for predicting watershed runoff.” J. Water Resour. Plann. Manage., 126(3), 156–166.
Bishop, C. M. (1994). “Mixture density networks.” Technical Rep. No. NCRG/94/004, Dept. of Computer Science and Applied Mathematics, Aston Univ., Birmingham, U.K.
Bishop, C. M. (1995). Neural networks for pattern recognition, Oxford University Press, New York.
Bowden, G. J., Dandy, G. C., and Maier, H. R. (2003). “Data transformation for neural network models in water resources applications.” J. Hydroinform., 5, 245–258.
Bowden, G. J., H. R. Maier, and G. C. Dandy (2002). “Optimal division of data for neural network models in water resources applications.” Water Resources Research, 38(2), 2.1–2.11.
Chang, N., and Makkeasorn, A. (2006). “Water demand analysis in urban region by neural network models.” Proc., 8th Water Distribution System Analysis Symp., USEPA/Univ. of Cincinnati, Cincinnati.
Greenaway, G., Guanlao, R., Bayda, N., and Zhang, Q. (2006). “Water distribution systems demand forecasting with pattern recognition.” Proc., 8th Water Distribution System Analysis Symp., USEPA/Univ. of Cincinnati, Cincinnati.
Hornik, K., Maxwell, S., and Halbert, W. (1989). “Multilayer feedforward networks are universal approximators.” Neural Networks, 2, 359–366.
House of Lords. (2006). “Water management.” HL Rep. No. 191-I, Vol. 1, House of Lords Science and Technology Committee, London.
Jain, S. K., Das, A., and Srivastava, D. K. (1999). “Application of ANN for reservoir inflow prediction and operation.” J. Water Resour. Plann. Manage., 125(5), 263–271.
Kendall, M., and Ord, J. K. (1990). Time series, 3rd Ed., Hodder and Stoughton Limited, Ky.
Maier, H. R., and Dandy, G. C. (2000). “Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications.” Environ. Modell. Software, 15, 101–124.
Mamlook, R., and Al-Jayyousi, O. (2003). Fuzzy sets analysis for leak detection in infrastructure systems: A proposed methodology, Clean Technology Environmental Policy, Vol. 6, No. 1, Springer Berlin, Heidelberg, 26–31.
McKenzie, R. and Seago, C. (2005). “Assessment of real losses in potable water distribution systems: Some recent developments.” Water Supply, 5(1), 33–40.
Misiunas, D., Vítkovský, J., Olsson, G., Lambert, M., and Simpson, A. (2006). “Failure monitoring in water distribution networks.” Water Sci. Technol., 53(4–5), 503–511.
Misiunas, D., Vítkovský, J., Olsson, G., Simpson, A. and Lambert, M. (2005). “Pipeline burst detection and location using a continuous monitoring of transients.” J. Water Resour. Plann. Manage., 131(4), 316–325.
Mounce, S. R. (2005). “A hybrid neural network fuzzy rule-based system applied to leak detection in water pipeline distribution networks.” Ph.D. thesis, Univ. of Bradford.
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 et al., eds., Taylor and Francis, London, 313–320.
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.” Inf. Fusion, 4(3), 217–229.
Mounce, S. R., Machell, J., and Boxall, J. (2006). “Development of artificial intelligence systems for analysis of water supply system data.” Proc., 8th Water Distribution System Analysis Symp., USEPA/Univ. of Cincinnati, Cincinnati.
Nabney, I. (2001). NETLAB: Algorithms for pattern recognition, Springer, New York.
Office of Water Services (OFWAT). (2006). ⟨www.ofwat.gov.uk⟩ (Sept. 28, 2008).
Shinozuka, M., and Liang, J. (2005). “Use of SCADA for damage detection of water delivery systems.” J. Eng. Mech., 131(3), 225–230.
Stoianov, I., Graham, N., Madden, S., Odenwald, T., and Stein, M. (2007). “WaterSense: Integrating sensor nets with enterprise decision support.” Water management challenges in global change, B. Ulanicki, et al., eds., Taylor and Francis, London, 123–126.
UK Water Industry Research (UKWIR). (2006). “Integrated network management roadmap.” Rep. No. 07/WM/18/4, UK Water Industry Research Ltd, London.
United Nations Environment Programme (UNEP). (2007). “Fourth global environment outlook: Environment for development assessment report.” United Nations Environment Programme, Progress Press Ltd, Malta.
Vandaele, W. (1983). Applied time series and Box-Jenkins models, Academic, San Diego.
Water Research Centre (WRc). (1994). “Managing leakage.” Rep. A, U.K. Water Industry Research Ltd./WRc, Wiltshire, England.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 136 • Issue 3 • May 2010
Pages: 309 - 318
Copyright
© 2010 ASCE.
History
Received: Sep 29, 2008
Accepted: Apr 30, 2009
Published online: May 15, 2009
Published in print: May 2010
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