An assessment model for urban resilience based on the pressure-state-response framework and BP-GA neural network

It has been widely appreciated that urban resilience is one of the core goals of urban development. Various approaches for evaluating the level of urban resilience have been developed recently. However, previous urban resilience assessment studies have mainly concentrated on the economy, society, infrastructure, and ecological environment, with very few considering the characteristics of the urban resilience regression process. Therefore, this research proposes a new assessment framework for urban resilience from the perspective of “pressure-state-response” to address this issue. And then, the methods of the BP neural network, genetic algorithm, Moran's index and the center of gravity model are combined to establish the assessment model of urban resilience. 31 provinces in Mainland China are selected as a case study to demonstrate the application of the assessment model. The calculation results indicate that the urban resilience level of all provinces in China is rising, and the provincial urban resilience development shows the characteristics of fluctuation. The trend of urban resilience shifted from north to south from 2013 to 2019, consistent with China's economic center of gravity moving from north to south. This study develops a new angle for evaluating urban resilience and provides effective policies toward urban resilience.