Jiaer Yang ^1,2 Pinglang Kou ^1,2* Xu Dong ^1,2 Ying Xia ² Qinchuan Gu ² Yuxiang Tao ¹ Jiangfan Feng ² Qin Ji ³ Weizao Wang ⁴ Ram Avtar ⁵

• ¹ Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
• ² Key Laboratory of Tourism Multisource Data Perception and Decision, Ministry of Culture and Tourism (TMDPD, MCT), Chongqing University of Posts and Telecommunications, Chongqing, China
• ³ Chongqing Key Laboratory of GIS Application, School of Geography and Tourism, Chongqing Normal University, Chongqing, China
• ⁴ School of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang, Hebei Province, China
• ⁵ Institute of Satellite Remote Sensing and Geographic Information Systems, Hokkaido University, Hokkaido, Japan

Current research on surface deformation in the Three Gorges Reservoir area is often limited to single regions or short-term observations, lacking systematic analysis of long-term deformation characteristics and driving factors for different surface types. This study employed SBAS-InSAR technology combined with various machine learning models to analyze and predict surface deformation in the Three Gorges Reservoir area of Fengjie County, Chongqing, China, from 2020 to 2022, revealing complex surface dynamic processes in the region. The study found that the average deformation rates for three typical surface types (riverside urban ground, riverside road slopes, and ancient landslides in the reservoir area) were -3.48±2.91 mm/yr, -5.19±3.62 mm/yr, and -6.02±4.55 mm/yr, respectively, with ancient landslide areas showing the most significant subsidence. Notably, a significant negative correlation was observed between reservoir water level decline and accelerated ground subsidence. Additionally, human activities, especially urbanization processes and road expansion projects, significantly exacerbated natural subsidence processes. In the application of prediction models, Long Short-Term Memory (LSTM) networks generally performed best in most cases but tended to overestimate deformation when predicting ancient landslides in the reservoir area, highlighting the challenges of long-term prediction in complex geological environments. These findings not only deepen our understanding of surface dynamics in the Three Gorges Reservoir area but also provide an important scientific basis for reservoir management, urban planning, and geological hazard risk assessment.