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http://hdl.handle.net/10397/104932
Title: | Data‑driven approaches for vibroacoustic localization of leaks in water distribution networks | Authors: | Liu, R Tariq, S Tijani, IA Fares, A Bakhtawar, B Fan, H Zhang, R Zayed, T |
Issue Date: | Mar-2024 | Source: | Environmental processes, Mar. 2024, v. 11, no. 1, 14 | 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. | Keywords: | Acoustic measurements Artificial neural network Data-driven Leak localization Micro-electromechanical System (MEMS) accelerometers Support vector machine Water distribution networks |
Publisher: | Springer | Journal: | Environmental processes | ISSN: | 2198-7491 | EISSN: | 2198-7505 | DOI: | 10.1007/s40710-024-00682-x | Rights: | © The Author(s) 2024 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/. 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. |
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