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dc.contributor.authorRomano, Michele
dc.contributor.authorKapelan, Zoran
dc.contributor.authorSavic, Dragan
dc.date.accessioned2013-05-31T09:51:18Z
dc.date.issued2012
dc.description.abstractThis paper presents a new methodology for the automated near real-time detection of pipe bursts and other events which induce similar abnormal pressure/flow variations (e.g., unauthorised consumptions) at the District Metered Area (DMA) level. The new methodology makes synergistic use of several self-learning Artificial Intelligence (AI) techniques and statistical data analysis tools including wavelets for de-noising of the recorded pressure/flow signals, Artificial Neural Networks (ANNs) for the short-term forecasting of pressure/flow signal values, Statistical Process Control (SPC) techniques for short and long term analysis of the pipe burst/other event-induced pressure/flow variations, and Bayesian Inference Systems (BISs) for inferring the probability of a pipe burst/other event occurrence and raising corresponding detection alarms. The methodology presented here is tested and verified on a case study involving several DMAs in the United Kingdom (UK) with both real-life pipe burst/other events and engineered (i.e., simulated by opening fire hydrants) pipe burst events. The results obtained illustrate that it can successfully identify these events in a fast and reliable manner with a low false alarm rate.en_GB
dc.identifier.doi10.1061/(ASCE)WR.1943-5452.0000339
dc.identifier.urihttp://hdl.handle.net/10871/9761
dc.language.isoenen_GB
dc.publisherAmerican Society of Civil Engineersen_GB
dc.relation.urlhttp://dx.doi.org/10.1061/(ASCE)WR.1943-5452.0000339en_GB
dc.titleAutomated Detection of Pipe Bursts and other Events in Water Distribution Systemsen_GB
dc.typeArticleen_GB
dc.date.available2013-05-31T09:51:18Z
dc.identifier.issn0733-9496
dc.descriptionCopyright 2012 by the American Society of Civil Engineersen_GB
dc.identifier.eissn1943-5452
dc.identifier.journalJournal of Water Resources Planning and Managementen_GB


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