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Water Consumption Prediction for City Pumping Station Using Neural Networks

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Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017 (ISPEM 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 637))

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Abstract

In this article, the ways to improve the efficiency of water supply in urban areas are considered and the need for water consumption prediction at the pumping stations output is shown. 58 models of artificial neural networks for prediction of the city water supply were built using the experimental data. As a result of the analysis of their performance, for future research, the one artificial network was chosen. Its absolute error of prediction does not exceed 3% in 35 h that fits the requirements. Based on the model of the artificial neural network, it was developed software for short-term prediction of water consumption at the output of the pumping station “Novyj Dvir”, Rivne city, Ukraine, for water supply of the city districts.

The original version of this chapter was revised: Misspelt co-author name has been corrected. The erratum to this chapter is available at https://doi.org/10.1007/978-3-319-64465-3_45

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Author information

Authors and Affiliations

  1. The National University of Water and Environmental Engineering, Rivne, Ukraine

    Volodymyr Drevetskyi, Marko Klepach & Vitalii Kutia

  2. Wroclaw University of Science and Technology, Wroclaw, Poland

    Vitalii Kutia

Authors
  1. Volodymyr Drevetskyi
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  2. Marko Klepach
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  3. Vitalii Kutia
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Corresponding author

Correspondence to Vitalii Kutia .

Editor information

Editors and Affiliations

  1. Faculty of Mechanical Engineering, Centre for Advanced Manufacturing Technologies, Fraunhofer Project Center, Wrocław University of Technology, Wrocław, Poland

    Anna Burduk

  2. Lublin University of Technology, Lublin, Poland

    Dariusz Mazurkiewicz

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Drevetskyi, V., Klepach, M., Kutia, V. (2018). Water Consumption Prediction for City Pumping Station Using Neural Networks. In: Burduk, A., Mazurkiewicz, D. (eds) Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017. ISPEM 2017. Advances in Intelligent Systems and Computing, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-319-64465-3_44

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  • DOI: https://doi.org/10.1007/978-3-319-64465-3_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64464-6

  • Online ISBN: 978-3-319-64465-3

  • eBook Packages: EngineeringEngineering (R0)

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