Integrating prospect theory and hesitant fuzzy linguistic preferences for enhanced urban flood resilience assessment: A case study of the tuojiang river Basin in western China

The intricate interplay between extreme climatic events and rapid urbanization has compounded the difficulties associated with urban flood resilience, prompting urgent improvements in cities' disaster preparedness and early warning systems. Existing assessment methodologies overlook human factors like decision-makers' psychological biases, hindering precise evaluations of flood defense capabilities. Motivated by the practical need for innovative disaster assessment tools, this study integrates hesitant fuzzy theory with prospect theory. This pioneering approach introduces interval-type indicators into the urban flood resilience assessment framework, establishing a quantitative model to gauge the correlation between urban resilience and various influencing factors. Using the Tuojiang River Basin in western China as a focal point, the research calculates the comprehensive urban flood resilience index for six cities along the Tuojiang River from 2002 to 2021. The findings indicate a consistent upward trajectory in the urban flood resilience index, mirroring government management practices. Notably, Chengdu boasts the highest urban flood resilience index at 1.4288, while Luzhou's index stands at −0.4376, reflecting a shortfall in the expected or desired level within the assessment framework, though without compromising index comparability. Moreover, both cities surpass the regional average of 0.36, categorizing them as high-resilience areas. Significantly, the efficacy of flood defense strategies in these areas correlates positively with economic growth (5.2 %). The model proposed in this study offers a thorough quantification of urban flood defense capabilities, assisting managers in formulating precise disaster mitigation strategies and providing scientific backing for integrated flood disaster management. •Prospect theory and hesitant fuzzy linguistic term set used to construct the UFR assessment framework.•Integrated interval-based indicators with decision-maker preferences into quantitative models for the assessment of UFR.•Improving the resource environment can significantly improve UFR.•Provided a set of policy recommendations to further improve basin's UFR.