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Leakage detection i...
Leakage detection in water distribution networks using machine-learning strategies
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- Sousa, Diego Perdigão (författare)
- a Department of Teleinformatics Engineering, Federal University of Ceara, Fortaleza, Brazil
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- Du, Rong, 1989- (författare)
- KTH,Nätverk och systemteknik,b School of Electrical Engineering and Computer Science and Digital Futures Research Center, KTH Royal Institute of Technology, Stockholm, Sweden
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- Barros da Silva Jr., José Mairton, Dr. 1990- (författare)
- KTH,Nätverk och systemteknik,Department of Electrical and Computer Engineering, Princeton University, Princeton, USA,Digital Futures Research Centre
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- Cavalcante, Charles Casimiro (författare)
- a Department of Teleinformatics Engineering, Federal University of Ceara, Fortaleza, Brazil
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- Fischione, Carlo (författare)
- KTH,Nätverk och systemteknik,b School of Electrical Engineering and Computer Science and Digital Futures Research Center, KTH Royal Institute of Technology, Stockholm, Sweden
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- Barros da Silva Junior, Jose Mairton, Ph.D. (författare)
- Uppsala universitet,Datorarkitektur och datorkommunikation,Princeton University and KTH Royal Institute of Technology
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(creator_code:org_t)
- 2023-02-21
- 2023
- Engelska.
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Ingår i: Water Science and Technology. - : IWA Publishing. - 1606-9749 .- 1607-0798. ; 23:3, s. 1115-1126
- Relaterad länk:
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https://doi.org/10.2...
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https://urn.kb.se/re...
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https://doi.org/10.2...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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%.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Vattenteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Water Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Information Systems (hsv//eng)
Nyckelord
- clustering
- leakage detection
- machine-learning
- supervised learning
- unsupervised learning
- water distribution network
- Machine learning
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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