## 1 Introduction  
Land subsidence (LS) is a global geological hazard caused by natural processes or human activities [1, 2]. Due to its often-overlooked and irreversible nature, it can lead to significant property damage and pose a threat to human safety [3]. Many cities in China, including Shanghai, Qingdao, Xi'an, and Chengdu, are affected by LS [4, 5, 6, 7]. Therefore, the large-scale, high-precision monitoring and predictions of urban LS are crucial. Traditional techniques such as leveling and global positioning systems (GPSs) can achieve an accuracy close to the millimeter level, but they fall short in large-scale and long-term land subsidence monitoring [8, 9]. In contrast, time-series interferometric synthetic aperture radar (TS-InSAR) excels at providing large-scale, high-precision, high-density, and cost-effective observations [10, 11, 12]. The primary TS-InSAR methods are persistent scatterer InSAR (PS-InSAR) and small baseline subset InSAR (SBAS-InSAR). The PS-InSAR method, based on permanent scatterers, reduces atmospheric disturbances and improves the monitoring accuracy to the millimeter level [13]. The SBAS-InSAR method, by shortening the spatial and temporal baselines of the interferograms, reduces decorrelation errors in areas with high deformation rates [14, 15]. Both methods have achieved significant results in urban land subsidence monitoring [16, 17]. Land subsidence data, as time-series data, can be predicted using various models [18, 19]. Prediction models for LS are mainly categorized into physical mechanism-basedand data-driven types. Compared to physical models [20; 21] that require complex geological parameters, data-driven models built on statistical principles and artificial intelligence technologies have been widely applied in urban land subsidence predictions (LSPs). Common methods include random forest [22; 23], BP neural networks [24], and recurrent neural networks (RNNs) [25]. RNNs and their variants, as deep learning models for time-series data, are well-suited for time-series prediction problems and have been used in InSAR data-based LSP research. For example, in 2020, [PERSON] et al. used LSTM (long short-term memory) models to predict surface changes in Beijing based on InSAR monitoring results, showing a good performance in areas with minor subsidence [26]. In 2021, [PERSON] et al. used PS-InSAR technology to monitor land subsidence at Beijing Capital International Airport from 2005 to 2010 and compared the results of multi-layer perceptron, RNN, and LSTM networks, finding LSTM to be the most effective [27]. That year, [PERSON] et al. developed a data-driven LSTM model to process PS-InSAR data for Wuhan, predicting and analyzing LS in the study area [8]. In 2022, [PERSON] et al. used PS-InSAR technology to study the evolution of land deformation in the Pudong International Airport reclamation area and established a deformation prediction model using LSTM for short-term predictions [28]. However, these models predict urban land subsidence data directly and overlook the different subsidence characteristics due to spatial variations. Complete-ensemble empirical-mode decomposition with adaptive noise (CEEMDAN) is an efficient, adaptive, noise-enhanced, empirical-mode decomposition method that obtains a set of intrinsic mode functions and a residual component through iterative filtering, making it more flexible and suitable for various types of nonlinear time series [29]. By decomposing subsidence data from different spatial locations using CEEMDAN, the model can deeply explore subsidence trends and distinguish seasonal trends in different regions, thus improving the prediction efficiency and accuracy. In summary, this study used the SBAS-InSAR method to obtain long-term LS information for Kunming City. Based on this, we proposed a multi-component temporal convolutional gate recurrent unit (MC-TCGRU) model, which combines CEEMDAN and deep neural networks.