Development and verification of an online artificial intelligence system for detection of bursts and other abnormal flows

Journal of Water Resources Planning and Management

Volumn 136, Issue 3, 2010, Pages 309-318

  Mounce, S R ^a   Boxall, J B ^a   Machell, J ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Artificial intelligence; Leakage; Neural networks; Water distribution systems; Water management

Indexed keywords

ALERT CORRELATION; ALERT SYSTEMS; ARTIFICIAL INTELLIGENCE SYSTEMS; ARTIFICIAL NEURAL NETWORK MODELS; BURST DETECTION; CONFIDENCE INTERVAL; CONFIDENCE VALUES; CONTROL ROOMS; CUSTOMER SERVICES; FLOW CONDITION; FLOW PROFILE; FUZZY INFERENCE SYSTEMS; GENERAL PACKET RADIO SERVICE; LEAKAGE; MANUAL ANALYSIS; MIXTURE DENSITY; NEW SYSTEM; ON-LINE APPLICATIONS; PRESSURE INCREASE; PROBABILITY DENSITIES; REALTIME FLOWS; TIME WINDOWS; TRIAL PERIOD; WATER DISTRIBUTION NETWORKS; WATER SERVICE;

ALARM SYSTEMS; CELLULAR TELEPHONE SYSTEMS; CUSTOMER SATISFACTION; DISTRIBUTED PARAMETER NETWORKS; FUZZY INFERENCE; FUZZY SYSTEMS; NEURAL NETWORKS; ONLINE SYSTEMS; PROBABILITY DENSITY FUNCTION; REPAIR; SALES; TELECOMMUNICATION SYSTEMS; WATER MANAGEMENT; WATER SUPPLY;

WATER DISTRIBUTION SYSTEMS;

ARTIFICIAL INTELLIGENCE; DATABASE; DETECTION METHOD; FUZZY MATHEMATICS; LEAKAGE; NUMERICAL MODEL; PLANNING METHOD; PROBABILITY DENSITY FUNCTION; RANKING; WATER FLOW; WATER MANAGEMENT; WATER PLANNING; WATER SUPPLY;

UNITED KINGDOM;

EID: 77954171515     PISSN: 07339496     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)WR.1943-5452.0000030     Document Type: Article

References (30)

1. Ackoff R.L. From data to wisdom. Journal of Applied Systems Analysis 1989, 16:3-9.
2. Anmala J. Zhang B. Govindaraju R. Comparison of ANNs and empirical approaches for predicting watershed runoff. J. Water Resour. Plann. Manage. 2000, 126(3):156-166. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2000)126:3(156).
3. Bishop C.M. Mixture density networks. 1994 Technical Rep. No.NCRG/94/004, Dept. of Computer Science and Applied Mathematics, Aston Univ., Birmingham, U.K.
4. Bishop C.M. Neural networks for pattern recognition 1995, Oxford University Press, New York.
5. Bowden G.J. Dandy G.C. Maier H.R. Data transformation for neural network models in water resources applications. J. Hydroinform. 2003, 5:245-258. ZZZZZZ, 1464-7141.,
6. Bowden G.J. Maier H.R. Dandy G.C. Optimal division of data for neural network models in water resources applications. Water Resources Research 2002, 38(2):2.1-2.11.
7. Chang N. Makkeasorn A. Water demand analysis in urban region by neural network models. Proc., 8th Water Distribution System Analysis Symp. 2006, USEPA/Univ. of Cincinnati, Cincinnati.
8. Greenaway G. Guanlao R. Bayda N. Zhang Q. Water distribution systems demand forecasting with pattern recognition. Proc., 8th Water Distribution System Analysis Symp. 2006 USEPA/Univ. of Cincinnati, Cincinnati.
9. Hornik K. Maxwell S. Halbert W. Multilayer feedforward networks are universal approximators. Neural Networks 1989, 2:359-366. NNETEB, 0893-6080, 10.1016/0893-6080(89)90020-8.
10. Water management. 2006, 1. House of Lords HL Rep. No.191-I, Vol., House of Lords Science and Technology Committee, London.
11. Jain S.K. Das A. Srivastava D.K. Application of ANN for reservoir inflow prediction and operation. J. Water Resour. Plann. Manage. 1999, 125(5):263-271. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(1999)125:5(263).
12. Kendall M. Ord J.K. Time series 1990, 3rd Ed., Hodder and Stoughton Limited, Ky.
13. Maier H.R. Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications. Environ. Modell. Software 2000, 15:101-124. EMSOFT, 1364-8152, 10.1016/S1364-8152(99)00007-9.
14. Mamlook R. Al-Jayyousi O. Fuzzy sets analysis for leak detection in infrastructure systems: A proposed methodology 2003, 6:26-31. Clean Technology Environmental Policy, Vol., No. 1, Springer Berlin, Heidelberg
15. McKenzie R. Seago C. Assessment of real losses in potable water distribution systems: Some recent developments. Water Supply 2005, 5(1):33-40. WASUDN, 0735-1917.
16. Misiunas D. Vítkovský J. Olsson G. Lambert M. Simpson A. Failure monitoring in water distribution networks. Water Sci. Technol. 2006, 53(4-5):503-511. WSTED4, 0273-1223, 10.2166/wst.2006.154.
17. Misiunas D. Vítkovský J. Olsson G. Simpson A. Lambert M. Pipeline burst detection and location using a continuous monitoring of transients. J. Water Resour. Plann. Manage. 2005, 131(4):316-325. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2005)131:4(316).
18. Mounce S.R. A hybrid neural network fuzzy rule-based system applied to leak detection in water pipeline distribution networks. 2005 Ph.D. thesis, Univ. of Bradford.
19. Mounce S.R. Boxall J.B. Machell J. 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 2007, 313-320. B. Ulanicki, eds., Taylor and Francis, London
20. Mounce S.R. Day A.J. Wood A.S. Khan A. Widdop P.D. Machell J. A neural network approach to burst detection. Water Sci. Technol. 2002, 45(4-5):237-246. WSTED4, 0273-1223.
21. Mounce S.R. Khan A. Wood A.S. Day A.J. Widdop P.D. Machell J. Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system. Inf. Fusion 2003, 4(3):217-229. ZZZZZZ, 1566-2535, 10.1016/S1566-2535(03)00034-4.
22. Mounce S.R. Machell J. Boxall J. Development of artificial intelligence systems for analysis of water supply system data. Proc., 8th Water Distribution System Analysis Symp. 2006, USEPA/Univ. of Cincinnati, Cincinnati.
23. Nabney I. NETLAB: Algorithms for pattern recognition 2001, Springer, New York.
24. 2006, Office of Water Services (OFWAT) (Sept. 28, 2008).
25. Shinozuka M. Liang J. Use of SCADA for damage detection of water delivery systems. J. Eng. Mech. 2005, 131(3):225-230. JENMDT, 0733-9399, 10.1061/(ASCE)0733-9399(2005)131:3(225).
26. Stoianov I. Graham N. Madden S. Odenwald T. Stein M. WaterSense: Integrating sensor nets with enterprise decision support. Water management challenges in global change 2007, 123-126. B. Ulanicki eds., Taylor and Francis, London
27. Integrated network management roadmap. 2006, UK Water Industry Research (UKWIR) Rep. No.07/WM/18/4, UK Water Industry Research Ltd, London.
28. Fourth global environment outlook: Environment for development assessment report. 2007, United Nations Environment Programme (UNEP) United Nations Environment Programme, Progress Press Ltd, Malta.
29. Vandaele W. Applied time series and Box-Jenkins models 1983, Academic, San Diego.
30. Managing leakage. 1994, Water Research Centre (WRc) Rep.A, U.K. Water Industry Research Ltd./WRc, Wiltshire, England.