Investigating river health across mountain to urban transitions using Pythagorean fuzzy cloud technique under uncertain environment

Abstract

River health assessment (RHA) is a robust methodology for ascertaining the health of riverine ecosystems, and offering solutions for river conservation and management. Currently, RHA studies have been conducted mainly in mountainous and urban areas, while investigations across mountain to urban transitions remain scarce and insufficient attention has been paid to RHA under uncertain environments. To systematically investigate the RHA across mountain to urban transitions, this study proposed Pythagorean fuzzy cloud (PFC) via integrating the cloud model and Pythagorean fuzzy sets. TOPSIS (Technique for order of preference by similarity to ideal solution) was expanded to the PFC environment to developed a novel PFC-TOPSIS model. The hybrid framework was then created to handle RHA with uncertainty, and the Pi River in China served as a case study. Results showed that the developed models broadened the capabilities of current techniques, and offered a more efficient way for dealing with RHA with uncertainty. In Pi River, the health status showed considerable spatial heterogeneity. Upper reaches were markedly healthier than the lower reaches. Evaluation indicators were at unsatisfactory health levels, with only 68.75% and 18.75% healthy attainment rates in the upper and lower reaches of Pi River, respectively. Meanwhile, 68.75% of the sampling sites were subhealthy and 31.25% were unhealthy. Our research emphasizes that urban development, agricultural practices and dam construction have affected river health in some areas, and appropriate measures are needed to reduce their impacts in Pi River.