Urban Ground Subsidence Monitoring and Prediction Using Time-Series InSAR and Machine Learning Approaches: A Case Study of Tianjin, China

Abstract

Urban ground subsidence, a major geo-hazard threatening sustainable urban development, has been increasingly reported worldwide, yet comprehensive investigations integrating multi-temporal ground deformation monitoring and predictive modeling are still lacking. This study aims to characterize the spatial-temporal evolution of ground subsidence in Tianjin's Jinnan District from 2016 to 2023 using 193 Sentinel-1A ascending images and the advanced Synthetic Aperture Radar Interferometry (InSAR) techniques of SBAS-InSAR and PS-InSAR. The maximum cumulative subsidence reached − 326.92 mm, with an average subsidence rate of -0.39 mm/year concentrated in industrial, commercial, and residential areas with high population density. Further analysis revealed that subway construction, human engineering activities, and rainfall were the primary drivers of ground subsidence in this region. Simultaneously, this study compared the predictive capabilities of five machine learning methods, including Support Vector Machine, Gradient Boosting Decision Tree, Random Forest, Extremely Randomized Tree, and Long Short-Term Memory (LSTM) neural network, for future ground subsidence. The LSTM-based prediction model exhibited the highest accuracy, with a root mean square error of 2.11 mm. Subdomain predictions generally outperformed the overall prediction, highlighting the benefits of reducing spatial heterogeneity. These findings provide insights into the mechanisms and patterns of urban ground subsidence, facilitating sustainable urban planning and infrastructure development.