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Water Quality Estimation from IoT Sensors Using a Meta-ensemble

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Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops (AIAI 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 677))

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Abstract

Water quality estimation using machine learning is a type of data analysis process that uses algorithms to identify patterns in large sets of data related to water quality. This can include identifying pollutants and other potential contamination that could negatively impact quality for drinking purposes, recreational activities or other uses. This helps ensure that the safety of water sources and the quality of recreational activities are constantly monitored and maintained. Thus, in this paper, a set of existing machine learning classifiers is applied to Internet of Things (IoT) sensor data on various water quality parameters, and the results are compared. Subsequently, a meta ensemble classifier that utilizes the soft voting technique of the best four previous classifiers is proposed to enhance estimation accuracy. According to results on the majority of the metrics used, this meta ensemble classifier outperforms all previously considered classifiers.

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Acknowledgements

This work was supported by the research project OpenDCO, “Open Data City Officer” (Project No.: 22022-1-CY01-KA220-HED-000089196, Erasmus+ KA2: KA220-HED - Cooperation partnerships in higher education)

Author information

Authors and Affiliations

  1. Department of Mathematics, University of Patras, Patras, Greece

    Gregory Davrazos & Sotiris Kotsiantis

  2. School of Science and Technology, Hellenic Open University, Patras, Greece

    Theodor Panagiotakopoulos

  3. School of Business, University of Nicosia, Nicosia, Cyprus

    Theodor Panagiotakopoulos

Authors
  1. Gregory Davrazos
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  2. Theodor Panagiotakopoulos
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  3. Sotiris Kotsiantis
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Corresponding author

Correspondence to Gregory Davrazos .

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  4. Hellenic Telecom Organization OTE, Athens, Greece

    Ioannis Chochliouros

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Davrazos, G., Panagiotakopoulos, T., Kotsiantis, S. (2023). Water Quality Estimation from IoT Sensors Using a Meta-ensemble. In: Maglogiannis, I., Iliadis, L., Papaleonidas, A., Chochliouros, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 677. Springer, Cham. https://doi.org/10.1007/978-3-031-34171-7_32

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  • DOI: https://doi.org/10.1007/978-3-031-34171-7_32

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