Urban resilience evaluation based on the DRIVING FORCE-PRESSURE-STATE-IMPACT-RESPONSE (DPSIR) framework and BP NEURAL NETWORK: A case study of Hubei Province

Building resilient cities has become an emerging risk management strategy, thus it is necessary to make a scientific evaluation on urban resilience. In this study, both the Driving Force-Pressure-State-Impact-Response (DPSIR) framework and the BP neural network were innovatively adopted to construct a comprehensive urban resilience evaluation model. Prefecture-level cities in Hubei Province were examined for empirical analysis. The results showed that: (1) Urban resilience is a dynamic process of change. The resilience level of cities in Hubei Province was influenced primarily by two major factors: driving force and response. (2) The urban resilience of cities in Hubei Province had been improving steadily from 2015 to 2021, but there was a spatial negative correlation among them. Owing to uneven development within Hubei Province, it can be apparently seen that Wuhan, the provincial capital, holds a dominant position. (3) Resource and environmental pressure has become the main obstacle to the construction of resilient cities in Wuhan. The primary limiting factors for other cities are the degree of socioeconomic growth and the capacity of the government to handle affairs. This study not only enriched the theory and methods of urban resilience evaluation, but also had important reference value for the government to formulate effective urban sustainable development strategies.