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Detection and Localization of Water Leaks in Water Nets Supported by an ICT System with Artificial Intelligence Methods as a Way Forward for Smart Cities

Author

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  • Izabela Rojek

    (Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, J. K. Chodkiewicza Street 30, 85-064 Bydgoszcz, Poland)

  • Jan Studzinski

    (Center of IT applications in environmental engineering, Systems Research Institute of Polish Academy of Sciences, 01-447 Warsaw, Poland)

Abstract

The last decade has seen the development of complex IT systems to support city management, i.e., the creation of so-called intelligent cities. These systems include modules dedicated to particular branches of municipal economy, such as urban transport, heating systems, energy systems, telecommunications, and finally water and sewage management. In turn, with regard to the latter branch, IT systems supporting the management of water supply and sewage networks and sewage treatment plants are being developed. This paper deals with the system concerning the urban water supply network, and in particular, with the subsystem for detecting and locating leakages on the water supply network, including so-called hidden leakages. These leaks cause the greatest water losses in networks, especially in old ones, with a very diverse age and material structure. In the proposed concept of the subsystem consisting of a GIS (Geographical Information System), SCADA (Supervisory Control and Data Acquisition) system and hydraulic model of the water supply network, an algorithm of leak detection and location based on the neural networks’ MLP (multi-layer perceptron) and Kohonen was developed. The algorithm has been tested on the hydraulic models of several municipal water supply networks.

Suggested Citation

  • Izabela Rojek & Jan Studzinski, 2019. "Detection and Localization of Water Leaks in Water Nets Supported by an ICT System with Artificial Intelligence Methods as a Way Forward for Smart Cities," Sustainability, MDPI, vol. 11(2), pages 1-13, January.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:2:p:518-:d:199182
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    Citations

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    Cited by:

    1. Allison Lassiter & Nicole Leonard, 2022. "A systematic review of municipal smart water for climate adaptation and mitigation," Environment and Planning B, , vol. 49(5), pages 1406-1430, June.
    2. KiJeon Nam & Pouya Ifaei & Sungku Heo & Gahee Rhee & Seungchul Lee & ChangKyoo Yoo, 2019. "An Efficient Burst Detection and Isolation Monitoring System for Water Distribution Networks Using Multivariate Statistical Techniques," Sustainability, MDPI, vol. 11(10), pages 1-17, May.
    3. Mariia Pankova & Aleksy Kwilinski & Nataliya Dalevska & Valentyna Khobta, 2023. "Modelling the Level of the Enterprise’ Resource Security Using Artificial Neural Networks," Virtual Economics, The London Academy of Science and Business, vol. 6(1), pages 71-91, March.
    4. Tariq Judeh & Isam Shahrour & Fadi Comair, 2022. "Smart Rainwater Harvesting for Sustainable Potable Water Supply in Arid and Semi-Arid Areas," Sustainability, MDPI, vol. 14(15), pages 1-22, July.
    5. Saeed Nosratabadi & Amir Mosavi & Ramin Keivani & Sina Ardabili & Farshid Aram, 2020. "State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability," Papers 2010.02670, arXiv.org.
    6. Jie Li & Megan Mocko, 2020. "Machine learning for a citizen data scientist: an experience with JMP," Journal of Marketing Analytics, Palgrave Macmillan, vol. 8(4), pages 267-279, December.

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