| Title: |
Comparative analysis of machine learning techniques in enhancing acoustic Noise Loggers’ leak detection |
| Authors: |
El-Zahab, S; Abdelkader, EM; Fares, A; Zayed, T |
| Contributors: |
Department of Building and Real Estate |
| Publisher Information: |
MDPI AG |
| Publication Year: |
2026 |
| Collection: |
Hong Kong Polytechnic University: PolyU Institutional Repository (PolyU IR) |
| Subject Terms: |
Acoustic noise loggers; Acoustics; Ensemble models; Leak detection; Machine learning; Water distribution networks |
| Description: |
202602 bcch ; Version of Record ; Others ; The authors gratefully acknowledge the support from the Innovation and Technology Fund (ITF), Hong Kong Special Administrative Region, under the Innovation and Technology Support Programme (ITSP), grant number ITS/067/19FP. The APC was not funded. ; Published ; CC |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
https://hdl.handle.net/10397/117621; 17; 16; 2427; OA_Scopus/WOS |
| DOI: |
10.3390/w17162427 |
| Availability: |
https://hdl.handle.net/10397/117621; https://doi.org/10.3390/w17162427 |
| Rights: |
Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). ; The following publication El-Zahab, S., Abdelkader, E. M., Fares, A., & Zayed, T. (2025). Comparative Analysis of Machine Learning Techniques in Enhancing Acoustic Noise Loggers’ Leak Detection. Water, 17(16), 2427 is available at https://doi.org/10.3390/w17162427. |
| Accession Number: |
edsbas.554BD3CB |
| Database: |
BASE |