Risk assessment of land subsidence in Shanghai municipality based on AHP and EWM

This study employs a multi-criteria decision analysis framework, integrating advanced technologies such as Remote Sensing (RS), Google Earth Engine (GEE), and Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR), to develop a comprehensive evaluation index system comprising fifteen indicators, which consider geological, hydrological, and anthropogenic factors. Urban land subsidence (LS) results in a reduction in ground elevation, compromising infrastructure integrity, disrupting the hydrological cycle, and posing significant risks to economic, demographic, and environmental security. This phenomenon is characterized by a certain degree of latency. In recent years, as Shanghai has undergone rapid urban expansion and high-density development, the issue of LS has become increasingly pronounced.

By applying matrix theory, the study utilizes the Analytic Hierarchy Process (AHP) and the Entropy Weight Method (EWM) to integrate subjective and objective weights, thereby determining the comprehensive weight for each indicator. Subsequently, the comprehensive natural disaster risk theory was employed to assess the risk levels across different regions within the study area, which were visualized using ArcGIS. The study area was classified into five risk categories: very low, low, medium, high, and very high, comprising 67.00%, 17.87%, 9.25%, 3.39%, and 2.48% of the total area, respectively. The results closely align with historical cumulative subsidence data and the current LS prevention map of Shanghai, confirming the validity and efficacy of the selected indicators and evaluation methodologies. The findings suggest that the overall risk level in the study area is relatively low, with high-risk zones concentrated in densely populated and economically urbanized central districts.