Entropy-weighted fuzzy and user perception-based framework for riverside greenway landscape quality assessment

Abstract Urban riverside greenways contribute significantly to ecological resilience, non-motorized mobility, and urban livability. However, comprehensive evaluation methods that integrate quantitative analysis with differentiated user perceptions remain limited. This study developed a combined entropy-weighted fuzzy evaluation and user perception framework to assess the landscape quality of the Zhenjiang riverside greenway, a site frequently visited by residents and tourists. A hierarchical indicator system was established with three dimensions: functional facilities, visual landscape, and cultural connotation, and 28 evaluation indicators reflecting the multidimensional attributes of greenway landscape quality. Indicator weights were determined using the entropy weight method, and fuzzy comprehensive evaluation was employed to quantify landscape performance along the greenway. Structured perception surveys were conducted with residents and tourists to assess their perceptions. The results indicated that visual landscape accounted for the highest importance (5.80), followed by functional facilities (4.07) and cultural connotation (3.87). Furthermore, there were significant differences in the landscape quality of sample spots; those with higher vegetation coverage, better accessibility, and complete facilities exhibited better evaluation performance. Due to the different demands of residents and tourists, the difference in perception scores for the same spot reached up to 1.11. Hence, this study demonstrated the effectiveness of integrating entropy-weighted fuzzy evaluation with user perception differentiation, supporting evidence-based and inclusive greenway planning. The proposed framework is a practical and transferable tool for efficiently and scientifically assessing urban riverside greenways.