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Related Experiment Video

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Integrating ensemble and contrastive learning with explainable AI for modeling eutrophication-driven algal risks.

Sultan K Salamah1, Marwan Kheimi2, Mohammad Zounemat-Kermani3

  • 1Civil Engineering Department, College of Engineering, Taibah University, P.O. Box 30002, Al-Madina, 42353, Saudi Arabia. Szain@taibahu.edu.sa.

Environmental Monitoring and Assessment
|April 14, 2026
PubMed
Summary

A new Composite Eutrophication Index (CEI) accurately predicts river eutrophication risk using machine learning. Ensemble models, particularly CatBoost, show superior performance in forecasting water quality and identifying key drivers of algal blooms.

Keywords:
Explainable artificial intelligenceHarmful algal bloomsIndividual conditional expectationInterpretable machine learningPartial dependence plots

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Area of Science:

  • Environmental Science
  • Water Quality Management
  • Machine Learning in Ecology

Background:

  • River eutrophication poses a significant threat to aquatic ecosystems and human health.
  • Accurate prediction of eutrophication risk is crucial for effective water resource management.
  • Existing methods for assessing eutrophication may not fully capture the complexity of contributing factors.

Purpose of the Study:

  • To develop and validate a Composite Eutrophication Index (CEI) for predicting river eutrophication risk.
  • To compare the performance of various machine learning models in forecasting water quality parameters related to eutrophication.
  • To identify the key drivers influencing river eutrophication using explainable AI techniques.

Main Methods:

  • Development of a Composite Eutrophication Index (CEI) using nitrate plus nitrite, orthophosphate, total phosphorus, and dissolved oxygen.
  • Application of supervised ensemble machine learning (Random Forest, XGBoost, CatBoost) and self-supervised contrastive learning (SimCLR, MoCo, SimSiam) models.
  • Utilizing stratified splitting for model training and validation, with performance evaluated against statistical MLR and TOPSIS methods.

Main Results:

  • Ensemble machine learning models, especially CatBoost, demonstrated superior accuracy in predicting river eutrophication risk compared to statistical methods.
  • CatBoost achieved the highest performance (TOPSIS rank 1, RMSE = 0.059, R² = 0.881).
  • Explainable AI (SHAP, PDP/ICE) identified physiochemical (pH, Total Kjeldahl Nitrogen) and hydrological factors (discharge, suspended sediment) as critical drivers of eutrophication.

Conclusions:

  • The developed Composite Eutrophication Index (CEI) and ensemble machine learning models offer a robust approach for predicting river eutrophication.
  • Accurate eutrophication prediction is vital for mitigating harmful algal blooms and persistent water pollution.
  • Understanding key drivers through explainable AI enhances targeted management strategies for water quality improvement.