Monitoring and Prediction of Surface Subsidence by Combining SSA-LSTM and TS-InSAR - A Case Study of Kunming Urban Area

Abstract To enhance our understanding of urban surface deformation mechanisms and to prevent geohazards, this study utilizes two time-series Interferometric Synthetic Aperture Radar (InSAR) methods with Sentinel-1 data: Persistent Scatterer-InSAR (PS-InSAR) and Small Baseline Subset-InSAR (SBAS-InSAR). These complementary methods jointly validate surface subsidence data in Kunming's urban area from 2020 to 2022. Utilizing this data, the study introduces and implements a Long Short Term Memory (LSTM) network model, which is optimized by the Sparrow Search Algorithm (SSA), to forecast and analyze future surface subsidence trends in Kunming. The results reveal that: (1) Kunming's urban area is undergoing persistent, large-scale surface subsidence, with cumulative subsidence measured at 122.8 mm. (2) Geographical location significantly influences the subsidence areas. (3) The subsidence in Area B is predominantly influenced by vehicular traffic. (4) The SSA-LSTM model accurately predicts the future trajectory of surface subsidence in Kunming's urban environment. (5) The complexity of the causes of surface settlement in Kunming is linked to natural factors, including geography, climate, and geology, as well as human activities such as rapid urbanization, groundwater extraction, subsurface construction, and mining operations. In conclusion, through a thorough, multifaceted analysis employing various methods, this study offers fresh insights and a robust scientific foundation for grasping the dynamics of surface subsidence in Kunming and for the anticipation and prevention of geological disasters. Subsequent research will continue to investigate the myriad factors influencing surface subsidence to more precisely forecast and mitigate the risks of geohazards. This work is vital for informed urban planning and the promotion f sustainable development.