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Artificial Neural Network Model for a Low Cost Failure Sensor: Performance Assessment in Pipeline Distribution
Khan, Asar Widdop, Peter D. Day, Andrew J. Wood, Alastair S. Mounce, Steve R. Machell, James
Khan, Asar
Widdop, Peter D.
Day, Andrew J.
Wood, Alastair S.
Mounce, Steve R.
Machell, James
Publication Date
2006
End of Embargo
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© 2006 World Enformatika Society. Reproduced in accordance with the publisher's self-archiving policy.
Peer-Reviewed
Yes
Open Access status
openAccess
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Abstract
This paper describes an automated event detection and
location system for water distribution pipelines which is based upon
low-cost sensor technology and signature analysis by an Artificial
Neural Network (ANN). The development of a low cost failure
sensor which measures the opacity or cloudiness of the local water
flow has been designed, developed and validated, and an ANN based
system is then described which uses time series data produced by
sensors to construct an empirical model for time series prediction and
classification of events. These two components have been installed,
tested and verified in an experimental site in a UK water distribution
system. Verification of the system has been achieved from a series of
simulated burst trials which have provided real data sets. It is
concluded that the system has potential in water distribution network
management.
Version
Published version
Citation
Khan A, Widdop PD, Day AJ, Wood AS, Mounce SR and Machell J (2006) Artificial Neural Network Model for a Low Cost Failure Sensor: Performance Assessment in Pipeline Distribution. Enformatika. 15: 195-201.
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Article