Predicting Surface Subsidence in Northern Huainan Based on a Hybrid LSTM–Transformer Model

As one of the world’s primary energy sources, coal has driven economic development but has also led to severe surface subsidence. Currently, many regions around the world face significant ground deformation risks due to ongoing or legacy mining activities. Accurate monitoring and trend prediction are critical for enhancing subsidence early-warning capabilities and urban resilience. The northern region of Huainan City exhibits a spatial pattern characterized by the coexistence of mining areas, urban areas, and decommissioned mining sites, among which the mining areas show more pronounced surface deformation due to prolonged mining activities. To fully understand the subsidence evolution characteristics and differences across various regions, an LSTM–Transformer prediction model was constructed based on SBAS-InSAR monitoring technology to predict the surface subsidence processes in the three types of areas separately. The results indicated that the subsidence rate and cumulative subsidence in the mining areas were significantly greater than those in the urban and decommissioned areas, demonstrating more intense deformation activity. The average subsidence rates for the mining areas, urban areas, and decommissioned mining sites were −57.42 mm/yr, −5.37 mm/yr, and −3.21 mm/yr, respectively. The model’s prediction results demonstrated good accuracy across different regions, with the root mean square errors (RMSEs) for the mining areas, urban areas, and decommissioned mining sites being 2.16 mm, 1.03 mm, and 0.22 mm, respectively. The study shows that the constructed LSTM–Transformer hybrid model not only possesses strong capability in fitting subsidence trends but will also provide a scientific basis for future monitoring and early warning of surface subsidence hazards.