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<p>Researchers commonly used the Index of Stream Condition (ISC) to assess the ecological state of river basins and rivers. However, the interdependence of factors across multiple hierarchical levels can rapidly influence the outcomes of ecological assessments. This study presents an integrated method that significantly enhances the river's health. Our holistic method addresses the issue of improving ecological assessments. Moreover, we performed dimensions’ reduction, inconsistency testing, and clustering within and between ISC3 sub-indexes and metrics for logical assessment and factor classification. The results show that the ISC3 sub-indexes can be reduced from five to three, achieving a 40% reduction in dimensionality. At the same time, the ISC3 metrics can be decreased from twenty-three to twelve, resulting in a 47.83% dimensionality reduction compared with the status quo. The discriminant coefficient (R<sup>2</sup>) significantly increased from 67.78 to 88.15%, a substantial improvement that reassures the effectiveness of our method. Moreover, all inconsistency index (InC.I.) values within and between ISC3's sub-indexes and metrics were below the critical value of 0.1, indicating high cluster homogeneity. These findings indicate that reducing the number of ISC3 index/sub-index factors and ensuring their homogeneity enhances their clustering effect. The proposed method will help future ecological inspections, assessments, and river health management, offering hope for significant improvements in these areas.</p>

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Enhancing River Health through an Integrated ISC Assessment Methodology Enhancing river health through ISC assessments: a holistic method

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Ching-Feng Chen,
  • Chia-Ming Fan

摘要

Researchers commonly used the Index of Stream Condition (ISC) to assess the ecological state of river basins and rivers. However, the interdependence of factors across multiple hierarchical levels can rapidly influence the outcomes of ecological assessments. This study presents an integrated method that significantly enhances the river's health. Our holistic method addresses the issue of improving ecological assessments. Moreover, we performed dimensions’ reduction, inconsistency testing, and clustering within and between ISC3 sub-indexes and metrics for logical assessment and factor classification. The results show that the ISC3 sub-indexes can be reduced from five to three, achieving a 40% reduction in dimensionality. At the same time, the ISC3 metrics can be decreased from twenty-three to twelve, resulting in a 47.83% dimensionality reduction compared with the status quo. The discriminant coefficient (R2) significantly increased from 67.78 to 88.15%, a substantial improvement that reassures the effectiveness of our method. Moreover, all inconsistency index (InC.I.) values within and between ISC3's sub-indexes and metrics were below the critical value of 0.1, indicating high cluster homogeneity. These findings indicate that reducing the number of ISC3 index/sub-index factors and ensuring their homogeneity enhances their clustering effect. The proposed method will help future ecological inspections, assessments, and river health management, offering hope for significant improvements in these areas.