A comprehensive comparative analysis of deep learning tools for modeling failures in smart water taps
dc.contributor.author | Offiong, NM | |
dc.contributor.author | Wu, Y | |
dc.contributor.author | Muniandy, D | |
dc.contributor.author | Memon, FA | |
dc.date.accessioned | 2023-08-22T13:32:36Z | |
dc.date.issued | 2021-08-17 | |
dc.date.updated | 2023-08-22T12:38:36Z | |
dc.description.abstract | Predicting early-stage failure in smart water taps (SWT) and selecting the most efficient tools to build failure prediction models are many challenges that water institutions face. In this study, three Deep Learning (DL) algorithms, i.e., the Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM), were selected to analyse and determine the most appropriate among them for failure prediction in SWTs. This study uses a historical dataset acquired from smart water withdrawal taps to determine the most efficient DL neural network architecture for failure prediction in the SWT, leading to a hybrid model's development. After a comprehensive evaluation of the three ML models, findings show that a hybrid combination of the CNN and Bi-LSTM (CNNBiLSTM) models is a better solution for investigating failures in the SWT. | en_GB |
dc.format.extent | 424-436 | |
dc.identifier.citation | Vol. 22(1), pp. 424-436 | en_GB |
dc.identifier.doi | https://doi.org/10.2166/ws.2021.261 | |
dc.identifier.uri | http://hdl.handle.net/10871/133841 | |
dc.identifier | ORCID: 0000-0003-0801-8443 (Wu, Y) | |
dc.identifier | ORCID: 0000-0002-0779-083X (Memon, FA) | |
dc.language.iso | en | en_GB |
dc.publisher | IWA Publishing | en_GB |
dc.rights | © 2022 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/). | en_GB |
dc.subject | Bi-LSTM | en_GB |
dc.subject | CNN | en_GB |
dc.subject | deep learning | en_GB |
dc.subject | failure prediction | en_GB |
dc.subject | LSTM | en_GB |
dc.subject | smart water taps | en_GB |
dc.subject | time-series | en_GB |
dc.title | A comprehensive comparative analysis of deep learning tools for modeling failures in smart water taps | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2023-08-22T13:32:36Z | |
dc.identifier.issn | 1606-9749 | |
dc.description | This is the final version. Available on open access from IWA Publishing via the DOI in this record | en_GB |
dc.description | Data availability statement: Data cannot be made publicly available; readers should contact the corresponding author for details. | en_GB |
dc.identifier.eissn | 1607-0798 | |
dc.identifier.journal | Water Supply | en_GB |
dc.relation.ispartof | Water Science & Technology Water Supply, 22(1) | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2021-08-05 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2021-08-17 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2023-08-22T13:30:54Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2023-08-22T13:32:40Z | |
refterms.panel | B | en_GB |
refterms.dateFirstOnline | 2021-08-17 |
Files in this item
This item appears in the following Collection(s)
Except where otherwise noted, this item's licence is described as © 2022 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).