About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Innovations and Interdisciplinary Solutions for Underserved Areas. 7th International Conference, InterSol 2024, Dakar, Senegal, July 3–4, 2024, Proceedings

Research Article

Analyzing Water’s Characteristics Health Impact with Classification Algorithms

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-86493-3_6,
        author={Khadim Gueye and Ndiouma Bame and Aliou Boly},
        title={Analyzing Water’s Characteristics Health Impact with Classification Algorithms},
        proceedings={Innovations and Interdisciplinary Solutions for Underserved Areas. 7th International Conference, InterSol 2024, Dakar, Senegal, July 3--4, 2024, Proceedings},
        proceedings_a={INTERSOL},
        year={2025},
        month={4},
        keywords={Anomaly detection water quality machine learning algorithms health parameters impact of physico-chemical parameters},
        doi={10.1007/978-3-031-86493-3_6}
    }
    
  • Khadim Gueye
    Ndiouma Bame
    Aliou Boly
    Year: 2025
    Analyzing Water’s Characteristics Health Impact with Classification Algorithms
    INTERSOL
    Springer
    DOI: 10.1007/978-3-031-86493-3_6
Khadim Gueye1,*, Ndiouma Bame1, Aliou Boly1
  • 1: Dept. Mathématiques et Informatique, Cheikh Anta Diop University
*Contact email: khadim40.gueye@ucad.edu.sn

Abstract

The water crisis is compounded by a number of factors, including population growth. In order to assess water potability, several indicators need to be taken into account during water quality evaluation. The World Health Organisation (WHO) sets concentration standards for each parameter to ensure that it is fit for drinking. The aim of this work is to take an in-depth look at these various water parameters, which have a significant impact on human health, and to understand how they influence water quality by using advanced machine learning techniques. The methodology consists on the one hand, to build a model for predicting the potability of water and on the other hand to study the impact of certain physico-chemical factors related to human health in this potability. The study was based on the use of three machine learning algorithms, namely Decision Tree, XGBoost and Random Forest, to analyze the impact of parameters such as pH, chlorine, chlorides, turbidity, nitrates, conductivity and fluoride. The results for the prediction model are promising especially for the Random Forest algorithm which gives the best performances. Regarding the impact of physico-chemical factors in the potability, all the algorithms place pH and chlorine at the top. Other parameters such as chlorides and turbidity are also significant, although their contribution is slightly lower than that of the previous characteristics.

Keywords
Anomaly detection water quality machine learning algorithms health parameters impact of physico-chemical parameters
Published
2025-04-21
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-86493-3_6
Copyright © 2024–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL