Land subsidence caused by human engineering activities is a serious problem worldwide. We selected Qian’an County as the study area to explore the evolution of land subsidence and predict its deformation trend. This study utilized synthetic aperture radar interferometry (InSAR) technology to process 64 Sentinel-1 data covering the area, and high-precision and high-resolution surface deformation data from January 2017 to December 2021 were obtained to analyze the deformation characteristics and evolution of land subsidence. Then, land subsidence was predicted using the intelligence neural network theory, machine learning methods, time-series prediction models, dynamic data processing techniques, and engineering geology of ground subsidence. This study developed three time-series prediction models: a support vector regression (SVR), a Holt Exponential Smoothing (Holt) model, and multi-layer perceptron (MLP) models. A time-series prediction analysis was conducted using the surface deformation data of the subsidence funnel area of Zhouzi Village, Qian’an County. In addition, the advantages and disadvantages of the three models were compared and analyzed. The results show that the three developed time-series data prediction models can effectively capture the time-series-related characteristics of surface deformation in the study area. The SVR and Holt models are suitable for analyzing fewer external interference factors and shorter periods, while the MLP model has high accuracy and universality, making it suitable for predicting both short-term and long-term surface deformation. Ultimately, our results are valuable for further research on land subsidence prediction.

Landslides and ground subsidence pose significant threats to the successful construction and operation of transmission line projects in the Loess Plateau region. This study aims to explore an accurate early identification method for geological hazards, providing support for the construction and smooth operation of the transmission project along the route from Baishui County, Weinan City, Shaanxi Province to Lantian County, Xi’an City, Shaanxi Province. Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was used to acquire the surface deformation data of the study area from 4 February 2018 to 21 May 2023. The deformation data were spatially analyzed through kernel density analysis, which quickly and intuitively identified 52 potential geological hazard points in the region, including eight landslides and 44 ground subsidence. Detailed field investigations of the hazards confirmed the accuracy of the identification results. A thorough analysis of typical hazards, such as landslide No. 9 and ground subsidence No. 29, revealed severe deformation, posing a threat to the proposed transmission project. This study indicates that combining InSAR, kernel density analysis, and field investigations can accurately and quickly identify geological hazards around transmission lines, providing support for the site selection and implementation of transmission projects.

Surface deformation monitoring is essential for controlling instability processes such as urban infrastructure deformation, mining-induced subsidence, and landslide deformation. However, missing data often disrupt the continuity of the various deformation time series and compromise the reliability of monitoring results. This issue is particularly critical in long-term landslide studies, where conventional missing data imputation methods often neglect the nonlinear characteristics of slope deformation and fail to account for external influences under complex environmental conditions. To address these limitations, this study proposes a deep learning-based imputation method that integrates multi-source monitoring data. A Seq2Seq LSTM (sequence-to-sequence long short-term memory) model is constructed to reconstruct missing deformation values, and a posterior correction module is integrated to optimize the preliminary outputs, thereby enhancing imputation accuracy. The proposed approach is validated using a case study of the southern dump slope landslide at the Hesigewula South Open-Pit Coal Mine in Inner Mongolia, China. Experimental results on the test set demonstrate that the Seq2Seq LSTM–Posterior Correction model significantly outperforms traditional methods such as linear regression and baseline LSTM models. This method offers an effective solution to data gaps in landslide deformation monitoring, demonstrating strong potential for accurate nonlinear imputation in complex environments and providing a practical approach for long-term InSAR-based landslide studies in areas affected by missing SAR data.