An Artificial Neural Network/Fuzzy Logic system for DMA flow meter data analysis providing burst identification and size estimation

Proceedings of the Combined International Conference of Computing and Control for the Water Industry, CCWI2007 and Sustainable Urban Water Management, SUWM2007

Volumn , Issue , 2007, Pages 313-320

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

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; FUZZY LOGIC; FUZZY NEURAL NETWORKS; WATER MANAGEMENT; WATER SUPPLY;

ANALYSIS SYSTEMS; ARTIFICIAL NEURAL NETWORKS; AUTOMATED APPROACHES; DATA ANALYSIS; FLOW DATUM; FLOW LEVELS; FLOW METERS; HUMAN DATUM; INPUT-OUTPUT MAPPINGS; LARGE DATA VOLUMES; LEAKAGE RATES; LOGIC SYSTEMS; MANUAL ANALYSIS; NEAR-REAL TIME; PIPE BURSTS; RAPID DETERMINATIONS; SIZE ESTIMATIONS; VALIDATION DATUM;

DATA FLOW ANALYSIS;

EID: 60749086018     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Bishop, C.M. (1994). 'Mixture density networks'. Technical report NCRG/94/004, Department of Computer Science and Applied Mathematics, Aston University, Birmingham, UK.
2. Blueprint for Water (2006). http://www.blueprintforwater.org.uk/
3. House of Lords (2006). Water Management, Volume I: Report, House of Lords Science and Technology Committee, HL Paper 191-1, 6th June 2006.
4. European Parliament (2000). EU Water Framework Directive, Directive 2000/60/EC, 23/10/2000. Official Journal (IJ L 327), 22 December 2000.
5. Hornick, K., Maxwell, S. & Halbert, W. (1989) Multilayer feedforward networks are universal approximators. Neural Networks 2: 359-366.
6. Husmeier, D., and Taylor, J.G. (1998). 'Neural Networks for Predicting Conditional Probability Densities: Improved Training Scheme Combining EM and RVFL', Neural Networks, 11(1): 89-116.
7. ICE (2006). State of the Nation 2006, ICE. http://www.ice.org.uk/state- of-the-nation/water-wastewater-uk- overview.asp
8. IPCC WG I (2007). Summary for policymakers Climate change 2007: Impacts, Adaptation and Vulnerability. Available from WWW: http://www.ipcc.ch/
9. Mamdani, E.H. and Assilian, S. (1975). 'An experiment in linguistic synthesis with a fuzzy logic controller.' International Journal of Man-Machine Studies, 7(1): 1-13.
10. Masters, T. (1993). Practical Neural Network Recipes in C++. San Diego: Academic Press, CA.
11. 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.' International Journal of Information Fusion, 4(3): 217-229.
12. Mounce, S.R. (2005). A hybrid neural network fuzzy rule-based system applied to leak detection in water pipeline distribution networks, PhD thesis, University of Bradford.
13. Mounce, S.R., and Machell, J. (2006). 'Burst detection using hydraulic data from water distribution systems with artificial neural networks.' Urban Water Journal, 3(1): 21-31.
14. Mounce, S. R., Machell, J. & Boxall, J. (2006) Development Of Artificial Intelligence Systems For Analysis Of Water Supply System Data. Proceedings of the 8th Water Distribution System Analysis Symposium, Cincinnati USA, August 27-30.
15. Nabney, I. (2001). NETLAB: Algorithms for pattern recognition, Springer-Verlag, UK.
16. Water Research Centre, Engineering and Operations Committee (1994a) UK Water Industry: managing leakage summary report (Report A). Swindon.