Abstract: [Purposes] To address the limitations of single monitoring techniques in terms of spatial coverage and stability for surface deformation monitoring of recently dredged and filled sites，a subsidence monitoring and prediction approach integrating GNSS and InSAR data with deep learning is proposed. [Methods] Taking an artificial reclaimed area in Doumen district，Zhuhai city，as the study area，the SBAS-InSAR technique was first applied to 22 Sentinel-1A images acquired from July 2022 to February 2024 to obtain subsidence time series and spatial distribution characteristics.Subsequently，a Kalman filter was employed to fuse GNSS and InSAR observations，generating deformation time series with improved stability.On this basis，a long short-term memory(LSTM)network optimized using the Adam algorithm was constructed for subsidence prediction，and the model performance was evaluated using MAE，RMSE，and R². [Findings] The results indicate that subsidence in the study area exhibits significant spatial heterogeneity.After data fusion，the standard deviations of deformation time series at three GNSS monitoring points decreased from 4.939 77， 2.276 89，and 2.660 43 mm to 3.863 44，2.000 49，and 2.298 29 mm，respectively.Compared with predictions based solely on InSAR data，the prediction errors using the fused data were reduced by 11.27%，13.61%，and 12.38%. [Conclusions] The proposed method effectively improves the stability of subsidence time series and the accuracy of subsidence prediction，providing technical support for subsidence monitoring and risk warning in coastal engineering areas．

Key words: SBAS-InSAR, GNSS, Kalman filtering, Adam-LSTM, subsidence prediction