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Watershed Planning within a Quantitative Scenario Analysis Framework
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Towards decision-ready explainable machine learning for water quality management using a consistency index and

Chao-Chin Chang1, Yuming Chen2, Chun-Yu Chen1

  • 1Department of Safety, Health and Environmental Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 824, Taiwan, ROC.

Environmental Research
|March 7, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a consistency index (CI) and recursive feature elimination (RFECV) to improve the reliability of explainable machine learning (XML) for water quality prediction. The methods enhance trust in environmental monitoring and water resource management decisions.

Keywords:
Consistency indexDecision-makingRFECVWater resources managementexplainable machine learning

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Area of Science:

  • Environmental Science
  • Data Science
  • Machine Learning

Background:

  • Reliable water quality prediction is crucial for environmental monitoring and water resource management.
  • Explainable machine learning (XML) aids in interpreting complex predictive models but faces challenges with inconsistent feature attributions and subjective feature selection.

Purpose of the Study:

  • To develop a unified consistency index (CI) and a data-driven cross-validated recursive feature elimination (RFECV) workflow.
  • To quantitatively assess and enhance the reliability of XML-based interpretations in water quality dynamics.

Main Methods:

  • Utilized a 30-year river-water dataset and nine machine-learning algorithms.
  • Evaluated two XML frameworks: correlation-based XML and RFECV-based XML.
  • Developed a consistency index (CI) to measure the agreement among explanation tools.

Main Results:

  • RFECV reduced input dimensionality by 69-85% while maintaining comparable predictive accuracy (RMSE).
  • Correlation-based XML showed strong rank-level agreement (CI values 0.42-0.72), while RFECV-based XML demonstrated tighter top-k agreement but weaker global ranking coherence (CI values 0.00-0.65).
  • Identified that reduced ranking stability in RFECV-based XML represents a practically relevant form of reliability, maintaining agreement on core drivers.

Conclusions:

  • The developed CI and RFECV workflow provide a quantitative diagnostic check for the robustness of explanations.
  • This approach enhances the clarity, trustworthiness, and practicality of XML-based water-quality assessments for decision-making.
  • Ensures that environmental monitoring and water resource management benefit from more reliable and interpretable machine learning models.