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Artificial Neural Network Model for a Low Cost Failure Sensor: Performance Assessment in Pipeline Distribution

By Asar Khan, Peter D. Widdop, Andrew J. Day, Alastair S. Wood, Steve R. Mounce and James Machell


YesThis paper describes an automated event detection and\ud location system for water distribution pipelines which is based upon\ud low-cost sensor technology and signature analysis by an Artificial\ud Neural Network (ANN). The development of a low cost failure\ud sensor which measures the opacity or cloudiness of the local water\ud flow has been designed, developed and validated, and an ANN based\ud system is then described which uses time series data produced by\ud sensors to construct an empirical model for time series prediction and\ud classification of events. These two components have been installed,\ud tested and verified in an experimental site in a UK water distribution\ud system. Verification of the system has been achieved from a series of\ud simulated burst trials which have provided real data sets. It is\ud concluded that the system has potential in water distribution network\ud management

Topics: Detection, ; Leakage, ; Neural networks, ; Sensors, ; Water distribution networks
Year: 2006
OAI identifier:
Provided by: Bradford Scholars

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