## 4 Results and Discussion
### Land Subsidence Time Series Prediction Results  
LS is influenced by a combination of factors, including geological and hydrogeological conditions, climate, and human activities. Consequently, the variations in the InSAR land subsidence monitoring data are shaped by the interplay of these complex environmental factors. The STL decomposition method, as referenced in the literature [41], breaks down the original sequence into three main components: trend, seasonal, and residual. This approach effectively stabilizes time-series data by accounting for its physical significance. In this study, the urban land subsidence data obtained through MT-InSAR technology exhibited certain abrupt changes, and some data had poor stability. The EMD method demonstrates strong robustness when dealing with time-series data with a poor stability, making it effective in stabilizing time-series data, thereby enhancing the accuracy of subsequent time-series predictions. The CEEMDAN method, a time-series decomposition algorithm developed from EMD, offers stronger feature decomposition capabilities and stability for complex time series. Therefore, this study compared two time-series decomposition methods, STL and EMD, to analyze the prediction performance of the MC-TCGRU model against the STL-TCGRU deep learning prediction model, the EMD-TCGRU deep learning prediction model, and the TCGRU model without time-series decomposition. The prediction results for the five LS points are shown in Figures 9-13, with the left side displaying the InSAR time-series  
Figure 8: Prediction results of each component based on CEEMDAN for point P1; (**a**) IMF\({}_{1}\) component; (**b**) IMF\({}_{2}\) component; (**c**) IMF\({}_{3}\) component; and (**d**) residual component. predictions for each subsidence point and the right side showing an enlarged view of the prediction results for the test set. Figure 11: Point P3 LS time-series prediction results (**left**) and enlarged view of test set (**right**). Figure 9: Point P1 LS time-series prediction results (**left**) and enlarged view of test set (**right**). As observed in the time-series prediction results, the MC-TCGRU model's predictions for all five points closely matched the actual InSAR observed deformation values, demonstrating the best predictive performance. The next best results were obtained by the EMD-TCGRU model, followed by the STL-TCGRU model, with the TCGRU model showing the poorest predictive performance. The three deep neural network prediction models that incorporated time-series decomposition algorithms all outperformed the TCGRU model without time-series decomposition, indicating that time-series decomposition effectively enhances the predictive ability of deep learning models. This improvement was because time-series decomposition essentially reduces the complexity of the original sequence by isolating the dominant and more regular data. Such data better represent the subsidence sequence's trend and are more easily captured by deep learning models. The prediction performance of the STL-TCGRU model, which uses STL decomposition, was overall inferior to that of the MC-TCGRU and EMD-TCGRU models. This was because STL decomposition more coarsely divides the original sequence into three main components--trend, seasonal, and residual--with fewer components than the EMD and CEEMDAN decomposition algorithms. This results in more noise and less pronounced periodicity, negatively affecting the model's final predictive performance. Compared to the MC-TCGRU model, the deep neural network prediction model based on STL decomposition exhibited a more noticeable prediction lag and underestimation at points of abrupt change. This was likely due to the seasonal component in the STL decomposition containing more noise. Figure 12: Point P4 LS time-series prediction results (**left**) and enlarged view of test set (**right**). Figure 13: Point P5 LS time-series prediction results (**left**) and enlarged view of test set (**right**).