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A Machine Learning - Based System for Determining Water Potability

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Distributed Computing and Artificial Intelligence, Special Sessions III - Intelligent Systems Applications, 21st International Conference (DCAI 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1173))

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Abstract

This research focuses on enhancing water potability classification through the integration of three machine learning techniques. A comparative analysis of diverse classification methods is conducted, incorporating multiple thresholds by employing a variable extraction approach. The primary objective is to streamline the input set, aiming for a significant reduction in computational costs and model complexity. This streamlined approach not only facilitates a more efficient training process but also implies a shorter duration for model training. To rigorously evaluate the model’s performance, a K-fold cross-validation is implemented within this framework. This comprehensive approach contributes to the advancement of water quality assessment methodologies, with potential implications for improving the efficiency and reliability of potability water classification models.

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Acknowledgement

Míriam Timiraos’s research was supported by the “Xunta de Galicia” through grants to industrial PhD (http://gain.xunta.gal/), under the “Doutoramento Industrial 2022” grant with reference: 04_IN606D_2022_ 2692965.

Antonio Díaz-Longueira’s research was supported by the Xunta de Galicia (Regional Government of Galicia) through grants to Ph.D. (http://gain.xunta.gal), under the “Axudas á etapa predoutoral” grant with reference: ED481A2023072.

 Grant PID2022-137152NB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU.

Xunta de Galicia. Grants for the consolidation and structuring of competitive research units, GPC (ED431B 2023/49)

CITIC, as a center accredited for excellence within the Galician University System and a member of the CIGUS Network, receives subsidies from the Department of Education, Science, Universities, and Vocational Training of the Xunta de Galicia. Additionally, it is co-financed by the EU through the FEDER Galicia 2021-27 operational program (Ref. ED431G 2023/01).

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Authors and Affiliations

  1. CTC, CITIC, Department of Industrial Engineering, University of A Coruña, Calle Mendizábal s/n, Campus de Esteiro, 15403, Ferrol, Spain

    Míriam Timiraos, Antonio Díaz-Longueira, Francisco Zayas-Gato, José-Luis Casteleiro-Roca & José Luis Calvo-Rolle

  2. LIDIA, CITIC, Department of Computer Sciences and Information Technologies, University of A Coruña, Calle Mendizábal s/n, Campus de Esteiro, 15403, Ferrol, Spain

    Óscar Fontenla-Romero

  3. Department of Water Technologies, National Technological Center, Fundación Instituto Tecnológico de Galicia, 15003, A Coruña, Spain

    Míriam Timiraos

Authors
  1. Míriam Timiraos
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  2. Antonio Díaz-Longueira
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  3. Francisco Zayas-Gato
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  4. José-Luis Casteleiro-Roca
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  5. Óscar Fontenla-Romero
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  6. José Luis Calvo-Rolle
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Corresponding authors

Correspondence to Míriam Timiraos or Antonio Díaz-Longueira .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Francisco Zayas-Gato

  2. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Antonio Díaz-Longueira

  3. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    José-Luis Casteleiro-Roca

  4. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Esteban Jove

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Timiraos, M., Díaz-Longueira, A., Zayas-Gato, F., Casteleiro-Roca, JL., Fontenla-Romero, Ó., Calvo-Rolle, J.L. (2024). A Machine Learning - Based System for Determining Water Potability. In: Zayas-Gato, F., Díaz-Longueira, A., Casteleiro-Roca, JL., Jove, E. (eds) Distributed Computing and Artificial Intelligence, Special Sessions III - Intelligent Systems Applications, 21st International Conference. DCAI 2024. Lecture Notes in Networks and Systems, vol 1173. Springer, Cham. https://doi.org/10.1007/978-3-031-73910-1_1

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