Burst detection using hydraulic data from water distribution systems with artificial neural networks

References

• Andersen , J. H. , Powell , R. S. and Marsh , J. F. 2001 . Constrained state estimation with applications in water distribution network monitoring . Int. J. Systems Science , 32 : 807 – 816 .
   Web of Science ®Google Scholar
• Babovic , V. and Drecourt , J. 2000 . Data mining approach to pipe burst risk assessment . Proceedings of Water Network Modelling for Optimal Design and Management CWS . 2000 .
   Google Scholar
• Bishop , C. M. 1995 . Neural Networks for Pattern Recognition , New York : Oxford University Press .
   Google Scholar
• Boger , Z. 1992 . Application of neural networks to water and wastewater treatment plant operation . Instrument Soc. Am. Trans. , 31 : 25 – 33 .
   Google Scholar
• Covas , D. , Graham , N. , Maksimovic , C. , Kapelan , Z. , Savic , D. A. and Walters , G. A. . An assessment of the application of inverse transient analysis for leak detection: Part II—collection and application of experimental data . Proceedings of Computer Control for Water Industry (CCWI) Conference . London, UK. pp. 79 – 87 .
   Google Scholar
• Cubero , R. G. 1991 . Neural networks for water demand time series forecasting . Proceedings International. Workshop IWANNI . 1991 . pp. 453 – 460 .
   Google Scholar
• Department of the Environment and the National Water Council . 1980 . Leakage control policy and practice, technical committee report, No. 26, DWI0190 . 1980 , London, UK.
   Google Scholar
• Gabrys , B. and Bargiela , A. 1999 . Neural networks based decision support in presence of uncertainties . J. Water Res. Planning Mgmt. , 125 : 272 – 280 .
   Web of Science ®Google Scholar
• Goldberg , K. Y. and Pearlmutter , B. A. 1989 . Using backpropagation with temporal windows to learn the dynamics of CMU direct-drive arm . Advances Neural Information Processing Systems , 1 : 356 – 363 .
   Google Scholar
• Gorman , R. P. and Sejnowski , T. J. 1988 . Analysis of hidden units in a layered network trained to classify sonar targets . Neural Networks , 1 : 75 – 89 .
   Web of Science ®Google Scholar
• Haykin , S. 1999 . Neural Networks – A Comprehensive Foundation. , 2nd ed. , New Jersey : Prentice Hall .
   Google Scholar
• Kaski , S. , Nikkila , J. and Kohonen , T. 1998 . Methods for interpreting a self organizing map in data analysis . Proceedings of the 6th European Symposium on Artificial Neural Networks, ESANN ‘98 . 1998 . pp. 185 – 190 .
   Google Scholar
• Kearns , M. 1996 . A bound on the error of cross validation using the approximation and estimation rates, with consequences for the training- test split . Advances Neural Information Processing Systems , 8 : 183 – 189 .
   Google Scholar
• Lapedes , A. and Farber , R. 1987 . “ Non-linear signal processing using neural networks. Report No. LA-UR-87-2662 ” . Los Alamos, New Mexico : Los Alamos National Laboratory .
   Google Scholar
• Linford , P. and Tattersall , G. D. 2001 . Advanced methods for leak detection and location: an investigation . Proceedings of the Workshop Monitoring, Modelling and Leakage Management in Water Distribution Networks . 2001 .
   Google Scholar
• Machell , J. 1997 . Research and development proposal for an on-line leakage detection system YWS internal report
   Google Scholar
• Masters , T. 1993 . Practical Neural Network Recipes in C++ , San Diego : Academic Press .
   Google Scholar
• Mounce , S. R. Implementation of a Kohonen network and an expert system interface . MSc thesis . University of Bradford . 1994
   Google Scholar
• 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 Science Tech. (IWA) , 45 : 237 – 246 .
   PubMed Web of Science ®Google Scholar
• 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 . Int. J. Information Fusion , 4 : 217 – 229 .
   Google Scholar
• Mozer , M. C. 1994 . “ Neural net architecture for temporal sequence processing ” . In Time Series Prediction: Predicting the Future and Understanding the Past , Edited by: Weigend , A. S. and Gershenfeld , N. A. 243 – 264 . Redwood City, CA : Addison-Wesley .
   Google Scholar
• OFWAT . Leakage figures Available online at: www.ofwat.gov.uk (accessed 2005)
   Google Scholar
• Rao , H. V. and Rao , V. B. 1995 . Neural Networks and Fuzzy Logic , New York : MIS Press .
   Google Scholar
• Rumelhart , D. E. , Hinton , G. E. and Williams , R. J. 1986 . Learning representations of back-propagation errors . Nature , 323 : 533 – 536 .
   Web of Science ®Google Scholar
• Shinozuka , M. and Liang , J. 2000 . Damage detection and location of water delivery by on-line water pressure monitoring . SPIE's 7th Annual International Symposium on Smart Structures and Materials . 2000 .
   Google Scholar
• Skipworth , P. J. , Saul , A. and Machell , J. 1999 . Predicting water quality in distribution using artificial neural networks . ICE J Water Maritime Energy , 136 : 1 – 8 .
   Web of Science ®Google Scholar
• Skipworth , P. J. , Saul , A. and Engelhardt , M. O. 2000 . Distribution network behaviour—extracting knowledge from data . Proceedings of Water Network Modelling for Optimal Design and Management CWS 2000 . 2000 .
   Google Scholar
• Stoianov , I. , Karney , B. , Covas , D. , Maksimović , C. and Graham , N. 2002 . Wavelet processing of transient signals for pipeline leak location and quantification . 1st Annual Environmental and Water Resources Systems Analysis (EWRSA) Symposium, A.S.C.E. EWRI Annual Conference . 2002 .
   Google Scholar
• Ulbricht , C. . State formation in neural networks for handling temporal information . PhD thesis . University of Vienna .
   Google Scholar
• Ulbricht , C. , Dorffner , G. and Lee , A. 1996 . “ Forecasting fetal heartbeats with neural networks ” . In Solving Engineering Problems with Neural Networks , Edited by: Bulsari , A. B. 403 – 406 . Turku : Systeemitekniikan seurary .
   Google Scholar
• Vandaele , W. 1983 . Applied Time Series and Box-Jenkins Models , Orlando : Academic Press .
   Google Scholar
• Waibel , A. , Hanazawa , T. , Hinton , G. , Shikano , K. and Lang , K. 1989 . Phoneme recognition using time delay neural networks . IEEE Transactions on Acoustics, Speech and Signal Processing , 37 : 328 – 339 .
   Google Scholar
• Water Research Centre, Engineering and Operations Committee . 1994 . “ UK Water Industry: managing leakage summary report (Report A) ” . Swindon
   Google Scholar
• Weigend , A. S. and Rumelhart , D. E. 1992 . “ Predicting sunspots and exchange rates with connectionist network ” . In Non- linear modelling and forecasting: Proceedings of the workshop on non-linear modelling and forecasting , Edited by: Gasdagl , M. and Eubank , S. 395 – 431 . New York : Addison-Wesley .
   Google Scholar
• WORC, State estimation . Water operational research centre web site , Brunel University . Available online at: http://www.brunel.ac.uk/depts/mes/Research/Groups/WORC/state.htm (accessed 1999)
   Google Scholar