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Machine Learning-Based Multifaceted Analysis Framework for Comparing and Selecting Water Quality Indices

Author

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  • Dana Simian

    (Lucian Blaga University of Sibiu)

  • Marin-Eusebiu Șerban

    (Lucian Blaga University of Sibiu)

  • Alina Bărbulescu

    (Transilvania University of Brașov)

Abstract

Water quality is essential to the population’s well-being, water resources management, and environmental development strategies. In this article, we propose a framework based on machine learning (ML) techniques for enhancing the assessment of water quality based on water quality indices (WQIs). It consists of three algorithms that could serve as a foundation for automating the evaluation of any resource based on indices and can operate locally or globally. Local-level algorithms assist in selecting suitable WQIs tailored to specific water sources and quality requirements, while global-level algorithm evaluates WQI robustness across diverse water sources. We also provide a warning system to mitigate differences in water quality evaluation using WQIs and a valuable tool (based on the features’ importance) for selecting ML models that prioritize the water parameters’ significance. The framework’s design draws upon conclusions from a case study involving the forecast and comparison of two WQIs for the Brahmaputra River. Any other data series, WQIs, and water parameters can be employed.

Suggested Citation

  • Dana Simian & Marin-Eusebiu Șerban & Alina Bărbulescu, 2025. "Machine Learning-Based Multifaceted Analysis Framework for Comparing and Selecting Water Quality Indices," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(2), pages 847-863, January.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:2:d:10.1007_s11269-024-03993-8
    DOI: 10.1007/s11269-024-03993-8
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    References listed on IDEAS

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    1. Muhammad Izhar Shah & Wesam Salah Alaloul & Abdulaziz Alqahtani & Ali Aldrees & Muhammad Ali Musarat & Muhammad Faisal Javed, 2021. "Predictive Modeling Approach for Surface Water Quality: Development and Comparison of Machine Learning Models," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    2. Margaret W. Gitau & Jingqiu Chen & Zhao Ma, 2016. "Water Quality Indices as Tools for Decision Making and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(8), pages 2591-2610, June.
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