Comprehensive assessment and prediction of urban resilience: a case study of China

Urban resilience is widely used to describe the capability of cities to fend off internal and external risks, reduce losses, and recover quickly. The assessment and prediction of urban resilience can help cities develop strategies and plans to deal with unknown disaster risks. This paper employs the method of combining the entropy weights method (EVM) and technology for order preference by similarity to an ideal solution to establish the urban resilience comprehensive evaluation model and uses the grey prediction model and back propagation neural network to predict the urban disaster resilience value. The results have suggested that the resilience of cities in different dimensions is generally high in eastern China, while the urban resilience in northeast China is not higher than the average, and the regions with an average level of resilience are concentrated in central China.