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<p>This paper explores the integration of machine learning with the trophic index (TRIX) to assess eutrophication and water quality, with a specific focus on the Mediterranean region and other ecologically sensitive areas. While various indices, such as the Water Quality Index and Pollution Index, exist, they often fall short of the TRIX index’s flexibility and specificity in aquatic eutrophication assessment. This paper highlights the benefits of combining machine learning models with TRIX for a more predictive, data-driven approach to water quality management.</p>

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A survey on TRIX index and machine learning applications in marine pollution context

  • 期刊论文

  • 发表日期：

    2025年5月7日

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  • Rim Zouari-Ktari,
  • Raïda Ktari,
  • Yosr Ghozzi,
  • Hajer Bendaya,
  • Marwa Hachicha

摘要

This paper explores the integration of machine learning with the trophic index (TRIX) to assess eutrophication and water quality, with a specific focus on the Mediterranean region and other ecologically sensitive areas. While various indices, such as the Water Quality Index and Pollution Index, exist, they often fall short of the TRIX index’s flexibility and specificity in aquatic eutrophication assessment. This paper highlights the benefits of combining machine learning models with TRIX for a more predictive, data-driven approach to water quality management.