Abstract: In view of the rapid urbanization in Hexi area of Nanjing and the few researches on settlement prediction in this area, this paper proposes a monitoring and prediction model of urban surface deformation based on small baseline subsets-interferometric synthetic aperture radar (SBAS-InSAR) and moving average-particle swarm optimization-backpropagation(MA-PSO-BP) neural network algorithm. The settlement monitoring of the Hexi area of Nanjing is carried out by using the 22 Sentinel-1A lift rail data from March 2020 to March 2022, the variable of the lifting rail in the study area is obtained, the trend and the causes of settlement in Hexi are analyzed, and the settlement value obtained is used as the sample input of the PSO-BP network model to construct a network prediction model. The results show that SBAS-InSAR technology can effectively monitor the long-term settlement of the city, there are different degrees of settlement in Hexi area of Nanjing, the settlement rate is -25.3～20.5 mm/a, compared with the historical settlement study, the settlement trend expands from north to south, combined with the settlement monitoring data of SBAS-InSAR, compared with BP neural network and PSO-BP neural network prediction model, the accuracy of the settlement prediction model after interpolation of sample data is the highest.

Key words: surface deformation monitoring, prediction model, moving average interpolatio, SBAS-InSAR, PSO-BP