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dc.contributor.authorKhan, Asar*
dc.contributor.authorWiddop, Peter D.*
dc.contributor.authorDay, Andrew J.*
dc.contributor.authorWood, Alastair S.*
dc.contributor.authorMounce, Steve R.*
dc.contributor.authorMachell, James*
dc.date.accessioned2008-10-30T08:25:58Z
dc.date.available2008-10-30T08:25:58Z
dc.date.issued2006
dc.identifier.citationKhan 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.en
dc.identifier.urihttp://hdl.handle.net/10454/819
dc.descriptionYesen
dc.description.abstractThis 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.en
dc.language.isoenen
dc.rights© 2006 World Enformatika Society. Reproduced in accordance with the publisher's self-archiving policy.en
dc.subjectDetectionen
dc.subject; Leakageen
dc.subject; Neural networksen
dc.subject; Sensorsen
dc.subject; Water distribution networksen
dc.titleArtificial Neural Network Model for a Low Cost Failure Sensor: Performance Assessment in Pipeline Distributionen
dc.status.refereedYesen
dc.typeArticleen
refterms.dateFOA2018-07-17T21:51:29Z


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