Assessing seismic risk is an essential element of urban risk management and urban spatial security work. In response to the issues posed by the complexity and openness of urban systems, the nonlinearity of driving factors, and sudden changes in geological processes that affect urban seismic research, this paper is based on a variety of intelligent algorithms to develop a hybrid intelligent model that integrates probability and vulnerability to evaluate and quantify the difference in the urban

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... atial units distribution of earthquake risk. We applied this model to Hefei, one of the few superlarge provincial capital cities on the "Tancheng-Lujiang" fault zone, one of the four major earthquake zones in China, which suffers frequent earthquakes. Our method combined the genetic algorithm (GA), particle swarm optimization (PSO), and backpropagation neural network methods (BP) to automatically calculate rules from inputted data on known seismic events and predict the probability of seismic events in unknown areas. Then, based on the analytic hierarchy process (AHP), spatial appraisal and valuation of environment and ecosystems method (SAVEE), and EMYCIN model, an urban seismic vulnerability was evaluated from the four perspectives of buildings, risk of secondary disasters, socioeconomic conditions, and urban emergency response capabilities. In the next step, the overall urban seismic risk was obtained by standardizing and superimposing seismic probability and vulnerability. Using the hybrid intelligent model, earthquake probability, seismic vulnerability, and overall seismic risk were obtained for Hefei, and the spatial characteristics of its overall seismic risk were examined. This study concludes that areas with very high, high, low, and very low earthquake risk in Hefei account for 8.10%, 31.90%, 40.94%, and 19.06% of its total area, respectively. Areas with very high earthquake risk are concentrated in the old city, the government affairs district, Science City, and Xinzhan District. This study concludes that govern [...]