Study on the Water Quality Evaluation of the Yiluo River based on Improved Fuzzy Comprehensive Evaluation

Abstract

Accurate river water-quality assessment is essential for watershed management, yet fixed-threshold and static weighting schemes may not adequately represent the fuzziness and gradual transitions of water-quality states. This study proposes a composite-weighted improved fuzzy comprehensive evaluation framework and applies it to the state-controlled Qilipu section of the Yiluo River using high-frequency monitoring data from 2022–2024. Five indicators are weighted via CRITIC and entropy methods and integrated into composite weights; semi-trapezoidal membership functions based on GB 3838-2002 are used to construct fuzzy relation matrices and derive comprehensive evaluation vectors. The results classify the study section as Class II in all three years under the maximum membership principle, indicating generally good water quality. Interannual weight and membership patterns further identify NH₃–N and TP as key constraints on improvement toward Class I, suggesting nutrient control as a management priority. Overall, the proposed approach provides an objective and interpretable workflow for translating monitoring data into water-quality grades and limiting-factor diagnostics.