Introduction Surface deformation in the Three Gorges Reservoir area poses significant threats to infrastructure and safety due to complex geological and hydrological factors. Despite existing studies, systematic exploration of long-term deformation characteristics and their driving mechanisms remains limited. This study combines SBAS-InSAR technology and machine learning to analyze and predict surface deformation in Fengjie County, Chongqing, China, between 2020 and 2022, focusing on riverside urban ground, riverside road slopes, and ancient landslides in the reservoir area.Methods SBAS-InSAR technology was applied to 36 Sentinel-1A images to monitor surface deformation, complemented by hydrological and meteorological data. Machine learning models-Random Forest (RF), Extremely Randomized Trees (ERT), Gradient Boosting Decision Tree (GBDT), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)-were evaluated using six metrics, including RMSE, R2, and SMAPE, to assess their predictive performance across diverse geological settings.Results Deformation rates for riverside urban ground, road slopes, and ancient landslides were -3.48 +/- 2.91 mm/yr, -5.19 +/- 3.62 mm/yr, and -6.02 +/- 4.55 mm/yr, respectively, with ancient landslides exhibiting the most pronounced deformation. A negative correlation was observed between reservoir water level decline and subsidence, highlighting the influence of seasonal hydrological adjustments. Urbanization and infrastructure development further exacerbated deformation processes. Among the models, LSTM demonstrated superior predictive accuracy but showed overestimation trends in ancient landslide areas.Discussion Reservoir water level adjustments emerged as a critical driver of subsidence, with rapid water level declines leading to increased pore pressure and soil compression. Seasonal effects were particularly evident, with higher subsidence rates during and after the rainy season. Human activities, including urbanization and road construction, significantly intensified deformation, disrupting natural geological conditions. Progressive slope failure linked to road expansion underscored the long-term impacts of engineering activities. For ancient landslides, accelerated deformation patterns were linked to prolonged drought and reservoir-induced hydrological changes. While LSTM models showed high accuracy, their limitations in complex geological settings highlight the need for hybrid approaches combining machine learning with physical models. Future research should emphasize developing integrated frameworks for long-term risk assessment and mitigation strategies in reservoir environments.Conclusions This study provides new insights into the complex surface dynamics in the Three Gorges Reservoir area, emphasizing the interplay of hydrological, geological, and anthropogenic factors. The findings highlight the need for adaptive management strategies and improved predictive models to mitigate subsidence risks.

Wave erosion is the main erosion type in the water -level fluctuation zone (WLFZ) of the Three Gorges Reservoir Area (TGRA). Despite vegetation can effectively mitigate wave erosion in the WLFZ, its influence on the wave force and wave erosion remains unclear. Therefore, the wave experiments were conducted under 3 Cynodon dactylon coverage rates (0, 30% and 60%) and 9 wave conditions (3 wave heights of 4, 6 and 8 cm combined with 3 wave periods of 1, 2 and 3 s) to analyse the wave force (expressed as the wave pressure on the slope surface and the pore water pressure in the slope) and wave erosion rate, and the factors influencing wave erosion were identified. The results indicated that the wave pressure, pore water pressure and wave erosion rate increased by 19.14%-104.75%, 16.84%-65.04% and 23.33%-91.64%, respectively, as wave height increases. The wave pressure decreased by 1.50%-31.23% followed by an increase by 22.05% to 87.10% with the increase of wave period, whereas the pore water pressure and wave erosion rate decreased by 28.33%-53.59% and 20.46%- 63.59%, respectively. However, these quantities decreased by 2.10%-50.84%, 17.06%-40.23% and 17.28%- 82.18%, respectively, with the increase of Cynodon dactylon coverage rate. It was also discovered that the pore water pressure and Cynodon dactylon coverage rate attained the highest positive and negative correlation coefficients with the wave erosion rate, respectively. In addition, pore water pressure accumulation is the most critical influence factor on wave erosion, and Cynodon dactylon could effectively reduce the pore water pressure via its roots, thus improving the slope wave erosion resistance. This study could be useful to understand the mechanism of plants on controlling wave erosion and could provide a scientific reference for wave erosion control and the ecological construction in the WLFZ.