LAUSR

Community resilience is a key index for describing the response of human habitat systems to hazards. Evaluating and enhancing the community resilience requires indicators, identification, and quantitative measurements, especially for urban flooding management. In this study, an advanced index framework for measuring community resilience to urban flooding is proposed, integrating the fuzzy Delphi method (FDM) and the analytic network process (ANP). Seven indicators (public facilities, spatial structure of land use, flood management organizations, rescue capability, accuracy of weather forecasts, vulnerable population, and individual capability) of community resilience are identified using the fuzzy Delphi method. The indicators are classified into four dimensions, and the weights are determined by the analytic network process. This approach is applied to three different types of communities, namely, a newly built neighborhood, an ancient college, and a flood-prone village in the city of Nanning, China, using data collected from questionnaires, interviews, and field investigations. The neighborhood (with a total averaged score of 2.13) has the largest community resilience to urban flooding, followed by the college (1.8), and finally the village (0.91). Flooding management organizations play a leading role in the urban flooding resilience of the neighborhood and college, while the vulnerable population has a great impact on the community resilience of the village. Results of the strategy analysis suggest that science and technology improvement (0.543) is more important than social–economic status improvement (0.325) and built-environment improvement (0.132) for mitigating urban hazards in Nanning. The proposed framework in this study contributes to the interdisciplinary understanding of community resilience for urban flooding and is expected to be applied to sustain urban planning and flood evacuations.