Contrastive learning method for leak detection in water distribution networks
Abstract Detecting and mitigating leaks in water distribution networks are vital for water conservation. Machine-learning-based (ML) acoustic leak detection models were introduced as effective alternatives for leak management. However, ML model training requires sufficient labeled data, which hinder...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | npj Clean Water |
| Online Access: | https://doi.org/10.1038/s41545-024-00406-6 |
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| _version_ | 1850056030336057344 |
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| author | Rongsheng Liu Tarek Zayed Rui Xiao |
| author_facet | Rongsheng Liu Tarek Zayed Rui Xiao |
| author_sort | Rongsheng Liu |
| collection | DOAJ |
| description | Abstract Detecting and mitigating leaks in water distribution networks are vital for water conservation. Machine-learning-based (ML) acoustic leak detection models were introduced as effective alternatives for leak management. However, ML model training requires sufficient labeled data, which hinders related development. To address this challenge, this study employed contrastive learning (CL) for leak detection using limited labeled signals. Experimental results indicate that flip-x and amplitude scaling are optimal combinations for contrastive learning. Besides, ablation and t-distributed stochastic neighbor embedding (t-SNE) results reveal that increasing the model depth does not always yield performance improvement, and five convolutional blocks are more suitable for the leak detection problem in this study. Comparison experiments demonstrate that contrastive learning outperforms supervised learning (SL) when trained with insufficient labeled data. The out-of-sample validation results indicate that the proposed leak detection model is robust and effective in unexplored pipelines. The proposed framework significantly advances ML-based leak detection research and supports sustainable water management practices. |
| format | Article |
| id | doaj-art-aee6d3f20a72428faed4e7f208a75c68 |
| institution | DOAJ |
| issn | 2059-7037 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Clean Water |
| spelling | doaj-art-aee6d3f20a72428faed4e7f208a75c682025-08-20T02:51:48ZengNature Portfolionpj Clean Water2059-70372024-11-017111310.1038/s41545-024-00406-6Contrastive learning method for leak detection in water distribution networksRongsheng Liu0Tarek Zayed1Rui Xiao2Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung HomDepartment of Building and Real Estate, The Hong Kong Polytechnic University, Hung HomDepartment of Building and Real Estate, The Hong Kong Polytechnic University, Hung HomAbstract Detecting and mitigating leaks in water distribution networks are vital for water conservation. Machine-learning-based (ML) acoustic leak detection models were introduced as effective alternatives for leak management. However, ML model training requires sufficient labeled data, which hinders related development. To address this challenge, this study employed contrastive learning (CL) for leak detection using limited labeled signals. Experimental results indicate that flip-x and amplitude scaling are optimal combinations for contrastive learning. Besides, ablation and t-distributed stochastic neighbor embedding (t-SNE) results reveal that increasing the model depth does not always yield performance improvement, and five convolutional blocks are more suitable for the leak detection problem in this study. Comparison experiments demonstrate that contrastive learning outperforms supervised learning (SL) when trained with insufficient labeled data. The out-of-sample validation results indicate that the proposed leak detection model is robust and effective in unexplored pipelines. The proposed framework significantly advances ML-based leak detection research and supports sustainable water management practices.https://doi.org/10.1038/s41545-024-00406-6 |
| spellingShingle | Rongsheng Liu Tarek Zayed Rui Xiao Contrastive learning method for leak detection in water distribution networks npj Clean Water |
| title | Contrastive learning method for leak detection in water distribution networks |
| title_full | Contrastive learning method for leak detection in water distribution networks |
| title_fullStr | Contrastive learning method for leak detection in water distribution networks |
| title_full_unstemmed | Contrastive learning method for leak detection in water distribution networks |
| title_short | Contrastive learning method for leak detection in water distribution networks |
| title_sort | contrastive learning method for leak detection in water distribution networks |
| url | https://doi.org/10.1038/s41545-024-00406-6 |
| work_keys_str_mv | AT rongshengliu contrastivelearningmethodforleakdetectioninwaterdistributionnetworks AT tarekzayed contrastivelearningmethodforleakdetectioninwaterdistributionnetworks AT ruixiao contrastivelearningmethodforleakdetectioninwaterdistributionnetworks |