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Tracking 30-year evolution of subsidence in Shanghai utilizing multi-sensor InSAR and random forest modelling

Journal Article (2025)
Authors

Can Lu (East China Normal University)

H. Xu (East China Normal University)

Qian Yao (East China Normal University)

Qing Liu (East China Normal University)

J.D. Bricker (University of Michigan, TU Delft - Hydraulic Structures and Flood Risk)

Sebastiaan Nicolaas Jonkman (TU Delft - Hydraulic Engineering)

Jie Yin (East China Normal University)

Jun Wang (East China Normal University)

Research Group
Hydraulic Structures and Flood Risk
To reference this document use:
https://doi.org/10.1016/j.jag.2025.104606
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Publication Year
2025
Language
English
Research Group
Hydraulic Structures and Flood Risk
Volume number
140
DOI:
https://doi.org/10.1016/j.jag.2025.104606
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Abstract

Land subsidence is a significant issue in many coastal megacities, including Shanghai, where it poses risks to infrastructure and economic stability. Although numerous studies have used SAR datasets to monitor land subsidence in Shanghai, multi-decadal displacement measurements obtained from multi-sensor SAR data remain unavailable. Moreover, the contributions and variations of driving factors behind the evolution of land subsidence remain poorly understood. This study employs multi-sensor SAR fusion method and a Random Forest model, along with Shapley Additive exPlanations (SHAP), to examine subsidence evolution and assess the influence of key drivers over the past 30 years. The results show that severe subsidence has spread from central urban areas to surrounding suburban regions, particularly in the eastern coastal and southern industrial zones in Shanghai. SHAP analysis identified that evapotranspiration, sediment thickness, and groundwater extraction were the dominant factors in the early stage of subsidence, while recent groundwater management and recharge practices have significantly mitigated the subsidence rate. These findings demonstrate the shifting importance of different subsidence factors over time and provide valuable insights for long-term prevention and control measures.