Time series land subsidence monitoring and prediction based on SBAS-InSAR and GeoTemporal transformer model

Land subsidence, the loss of elevation of the earth's surface caused by natural and human-induced factors, has become a significant global concern. It poses substantial threats to urban planning, construction, and sustainable development. Monitoring and predicting regional land subsidence are particularly crucial. Interferometric Synthetic Aperture Radar (InSAR) and deep learning provide valuable insights into monitoring and predicting land subsidence. However, methods for accurate and long-term monitoring and predicting time series land subsidence still have limitations. Firstly, most models only utilize historical data and overlook the combined effects of various factors, including human activities and urbanization. Secondly, the spatiotemporal correlation of subsidence across different locations and times is underestimated. Thirdly, the nonlinearity of land subsidence is not adequately addressed. To address these challenges, this study assesses land deformation patterns from January 2018 to December 2022, using Sentinel-1 InSAR data processed through Small Baseline Subset-InSAR (SBAS-InSAR). The result shows that the annual average deformation rate ranged from -6.39 to 8.27 mm/year, with maximum cumulative subsidence and uplift of 27.62 mm and 36.62 mm, respectively. Subsequently, a GeoTemporal Transformer (GTformer) model based on the Transformer model is proposed. It captures nonlinearities and spatiotemporal correlations between land subsidence and influencing factors by generating spatiotemporal distance matrices. The results demonstrate the efficacy of the GTformer model in improving prediction accuracy by incorporating urbanization factors and constructing spatiotemporal distance matrices. Compared with traditional machine learning models, the R2 of GTformer has increased by at least 14.6%, and compared with the standard Transformer, it has increased by 4%. The predictions closely align with observed subsidence patterns, highlighting the reliability. Moreover, this study underscores the critical role of urbanization factors in land subsidence mechanisms. The GTformer model provides a novel approach that integrates multiple factors and spatiotemporal correlation to predict land subsidence. The methodology offers a valuable tool for urban planners and decision-makers to effectively manage urban development and mitigate geological disaster risks.