DC FieldValueLanguage
dc.contributorDepartment of Building and Real Estateen_US
dc.creatorTariq, Sen_US
dc.creatorBakhtawar, Ben_US
dc.creatorZayed, Ten_US
dc.date.accessioned2021-11-19T01:29:48Z-
dc.date.available2021-11-19T01:29:48Z-
dc.identifier.issn0048-9697en_US
dc.identifier.urihttp://hdl.handle.net/10397/91610-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectAccelerometersen_US
dc.subjectEnsemblesen_US
dc.subjectLeak detectionen_US
dc.subjectMachine learningen_US
dc.subjectMEMSen_US
dc.subjectReal networksen_US
dc.titleData-driven application of MEMS-based accelerometers for leak detection in water distribution networksen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.doi10.1016/j.scitotenv.2021.151110en_US
dcterms.abstractWater scarcity is a global concern; 68 countries are facing extremely-high to medium-high risk of water stress. In this era of crisis, where water conservation is an absolute necessity, the water distribution networks (WDNs) globally are experiencing significant leaks. These leaks cause tremendous financial loss and unacceptable environmental hazards, thus further aggravating the water scarcity situation. To minimize such damage, the adoption of advanced technologies and methodologies for leak detection in the WDNs is absolutely necessary. In this regard, we have investigated the application of cost-effective MEMS-based accelerometers. Experiments were conducted on real networks (metal and non-metal pipes), over the course of ten months, and the acquired acceleration signals were analyzed using a monitoring algorithm. Monitoring index efficiencies and standard deviations for every leak and no-leak case was extracted. Two individual [KNN and Decision Tree] and two ensembles [Random Forest and Adaboost (Decision Tree)] based machine learning models were developed for the accurate classification of the leak and no-leak cases using extracted features; and separate models were developed for metal and non-metal pipes. Random Forest outperformed the other machine learning models and the overall accuracy reached 100% for metal pipes and 94.93% for non-metal pipes. The machine learning models were further validated using unseen/unlabeled cases and were highly effective in detecting leaks. This study demonstrated the applicability of MEMS-based accelerometers for leak detection and established real network-based machine learning models thereby contributing to the research scarcity in this important area.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationScience of the total environment, Available online 22 October 2021, In Press, 151110, https://doi.org/10.1016/j.scitotenv.2021.151110en_US
dcterms.isPartOfScience of the total environmenten_US
dcterms.issued2021-
dc.identifier.scopus2-s2.0-85117824868-
dc.identifier.pmid34688733-
dc.identifier.eissn1879-1026en_US
dc.identifier.artn151110en_US
dc.description.validate202111 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1076-n01-
dc.identifier.SubFormID43886-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextITS/067/19FPen_US
dc.description.pubStatusEarly releaseen_US
dc.date.embargo0000-00-00 (to be updated)en_US
Appears in Collections:Journal/Magazine Article
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.