Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/104932
DC Field | Value | Language |
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dc.contributor | Department of Building and Real Estate | en_US |
dc.creator | Liu, R | en_US |
dc.creator | Tariq, S | en_US |
dc.creator | Tijani, IA | en_US |
dc.creator | Fares, A | en_US |
dc.creator | Bakhtawar, B | en_US |
dc.creator | Fan, H | en_US |
dc.creator | Zhang, R | en_US |
dc.creator | Zayed, T | en_US |
dc.date.accessioned | 2024-03-07T08:50:04Z | - |
dc.date.available | 2024-03-07T08:50:04Z | - |
dc.identifier.issn | 2198-7491 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/104932 | - |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.rights | © The Author(s) 2024 | en_US |
dc.rights | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | en_US |
dc.rights | The following publication Liu, R., Tariq, S., Tijani, I. A., Fares, A., Bakhtawar, B., Fan, H., ... & Zayed, T. (2024). Data-Driven Approaches for Vibroacoustic Localization of Leaks in Water Distribution Networks. Environmental Processes, 11(1), 14 is available at https://doi.org/10.1007/s40710-024-00682-x. | en_US |
dc.subject | Acoustic measurements | en_US |
dc.subject | Artificial neural network | en_US |
dc.subject | Data-driven | en_US |
dc.subject | Leak localization | en_US |
dc.subject | Micro-electromechanical System (MEMS) accelerometers | en_US |
dc.subject | Support vector machine | en_US |
dc.subject | Water distribution networks | en_US |
dc.title | Data‑driven approaches for vibroacoustic localization of leaks in water distribution networks | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 11 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.doi | 10.1007/s40710-024-00682-x | en_US |
dcterms.abstract | This study aims to propose Micro-electromechanical System (MEMS) accelerometers for leak localization in the water distribution network and assess the performance of machine learning models in accurately estimating leak locations. Intensive field experimentation was conducted to collect data for model development. Machine learning algorithms were employed to develop leak localization models, specifically artificial neural network (ANN) and support vector machine (SVM). Seventeen time-domain and frequency-domain features were extracted, and feature selection was performed using the backward elimination method. The results indicate that the ANN and SVM models are suitable classifiers for localizing leak distance. Both models achieved leak location predictions with over 80% accuracy, and the mean absolute errors were measured at 0.858 and 0.95 for the ANN and SVM models, respectively. The validation results demonstrated that the models maintained accuracies close to 80% when the distance between sensors and the leak was less than 15 m. However, the performance of the model deteriorates when leaks occur at distances greater than 15 m. This study demonstrates the applicability of MEMS accelerometers for leak localization in water distribution networks. The findings highlight the promising potential of employing MEMS accelerometers-based ANN and SVM models for accurate leak localization in urban networks, even under real-world, uncontrolled conditions. However, the current model exhibits limited performance in long-distance leak localization, requiring further research to address and resolve this issue. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Environmental processes, Mar. 2024, v. 11, no. 1, 14 | en_US |
dcterms.isPartOf | Environmental processes | en_US |
dcterms.issued | 2024-03 | - |
dc.identifier.scopus | 2-s2.0-85185891071 | - |
dc.identifier.eissn | 2198-7505 | en_US |
dc.identifier.artn | 14 | en_US |
dc.description.validate | 202403 bckw | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_TA | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Innovation and Technology Fund (Innovation and Technology Support Programme (ITSP)), Hong Kong; Water Supplies Department, Hong Kong | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.TA | Springer Nature (2024) | en_US |
dc.description.oaCategory | TA | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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s40710-024-00682-x.pdf | 1.9 MB | Adobe PDF | View/Open |
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