利用C-LSTM的时序InSAR地表形变趋势分析及预测方法OA北大核心

Time-series interferometric synthetic aperture radar(TS-InSAR)technology has been widely used in ground deformation monitoring and prediction.However,current research remains insufficient in the correlation and temporal lag between groundwater and ground deformation.Moreover,InSAR-based prediction models for ground deformation mostly rely on a single InSAR data,which limits the prediction accuracy and generalization ability of the models.To address these challenges,this study proposed a combination-long short-term memory(C-LSTM)model that integrates groundwater level,rainfall,and InSAR deformation data.This model was employed to evaluate the prediction and accuracy of single-factor and multi-factor models,respectively.The results revealed a temporal lag between ground deformation and changes in groundwater level.The optimal feature combination,obtained through model training using groundwater and rainfall data,exhibited significant improvements in prediction accuracy compared to single-factor predictions,with the coefficient of determination(R2)increasing by 2.45%,1.52%,4.16%,8.08%,5.08%,and 1.45%respectively.The model enhances the prediction accuracy of ground deformation by incorporating model feature combinations with high correlation with ground deformation.