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A platform for research: civil engineering, architecture and urbanism
Leakage detection in water distribution networks using machine-learning strategies
This work proposes a reliable leakage detection methodology for water distribution networks (WDNs) using machine-learning strategies. Our solution aims at detecting leakage in WDNs using efficient machine-learning strategies. We analyze pressure measurements from pumps in district metered areas (DMAs) in Stockholm, Sweden, where we consider a residential DMA of the water distribution network. Our proposed methodology uses learning strategies from unsupervised learning (K-means and cluster validation techniques), and supervised learning (learning vector quantization algorithms). The learning strategies we propose have low complexity, and the numerical experiments show the potential of using machine-learning strategies in leakage detection for monitored WDNs. Specifically, our experiments show that the proposed learning strategies are able to obtain correct classification rates up to 93.98%. HIGHLIGHTS Leakage detection in water distribution networks using efficient machine-learning strategies.; We analyze pressure measurements from pumps in district-metered areas in Stockholm, Sweden, where we consider a monitored subarea of the water distribution network.; Our proposal can be applied to leakage detection scenarios where we have access to water pressure measurements at different points of the WDN.;
Leakage detection in water distribution networks using machine-learning strategies
This work proposes a reliable leakage detection methodology for water distribution networks (WDNs) using machine-learning strategies. Our solution aims at detecting leakage in WDNs using efficient machine-learning strategies. We analyze pressure measurements from pumps in district metered areas (DMAs) in Stockholm, Sweden, where we consider a residential DMA of the water distribution network. Our proposed methodology uses learning strategies from unsupervised learning (K-means and cluster validation techniques), and supervised learning (learning vector quantization algorithms). The learning strategies we propose have low complexity, and the numerical experiments show the potential of using machine-learning strategies in leakage detection for monitored WDNs. Specifically, our experiments show that the proposed learning strategies are able to obtain correct classification rates up to 93.98%. HIGHLIGHTS Leakage detection in water distribution networks using efficient machine-learning strategies.; We analyze pressure measurements from pumps in district-metered areas in Stockholm, Sweden, where we consider a monitored subarea of the water distribution network.; Our proposal can be applied to leakage detection scenarios where we have access to water pressure measurements at different points of the WDN.;
Leakage detection in water distribution networks using machine-learning strategies
Diego Perdigão Sousa (author) / Rong Du (author) / José Mairton Barros da Silva Jr (author) / Charles Casimiro Cavalcante (author) / Carlo Fischione (author)
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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