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Conductivity Classification Using Machine Learning Algorithms in the “Bramianon” Dam

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Engineering Applications of Neural Networks (EANN 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1826))

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Abstract

During the “water cycle” process, inorganic as well as organic substances are dissolved, which is completely normal. Organic substances can originate from decaying tree leaves that fall into rivers and lakes, from sewage from living organisms that live in water (e.g. fish) and human waste. Inorganic substances can come from lead and copper in water pipes, from pesticides and generally from various human activities. All these elements contribute to increase of water conductivity. The higher the conductivity in water, the more dangerous it becomes for humans [4]. The purpose of this research is to evaluate and classify water conductivity levels at the “Bramianon” dam of Crete, with the development of powerful Machine Learning models capable of successfully assigning three labels “Low”, “Medium”, “High”.

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Authors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Kiourt Nichat, Lazaros Iliadis & Antonios Papaleonidas

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  1. Kiourt Nichat
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  2. Lazaros Iliadis
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  3. Antonios Papaleonidas
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Correspondence to Kiourt Nichat .

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Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  3. University of Leon, León, Spain

    Serafin Alonso

  4. Teesside University, Middlesbrough, UK

    Chrisina Jayne

  5. University of the West of England, Bristol, UK

    Elias Pimenidis

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Nichat, K., Iliadis, L., Papaleonidas, A. (2023). Conductivity Classification Using Machine Learning Algorithms in the “Bramianon” Dam. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. https://doi.org/10.1007/978-3-031-34204-2_9

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  • DOI: https://doi.org/10.1007/978-3-031-34204-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34203-5

  • Online ISBN: 978-3-031-34204-2

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