Ground subsidence risk assessment method using PS-InSAR and LightGBM: a case study of Shanghai metro network

ABSTRACT

Ground subsidence is a common geological hazard in urban areas that endangers the safety of infrastructure, such as subways. In this study, the ground subsidence risk assessment method considering both ground subsidence intensity and susceptibility is proposed and applied to assess ground subsidence risk of the Shanghai Metro network. Initially, PS-InSAR is used for the ground subsidence survey in the Shanghai Metro area. Subsequently, ten subsidence causal factors are collected, and the LightGBM machine learning algorithm is employed to conduct the ground subsidence susceptibility analysis. Then, a risk matrix is introduced to define ground subsidence risk by combining subsidence intensity and susceptibility. Finally, the risk map is generated in ArcGIS and classified into five levels. The assessment results were used to identify ground subsidence risk at different scales. The results indicate that the risk is higher in the southwest part of the study area, and the ground subsidence risk of the metro network exhibits a regional-related characteristic. On-site investigations were conducted to verify the results. The method enables fast ground subsidence assessment over a large area at a low cost and the assessment results can provide data for the prevention and management of ground subsidence hazards in the city.

KEYWORDS:

• Ground subsidence
• risk assessment
• metro network
• PS-InSAR
• LightGBM

Acknowledgements

The authors sincerely thank the European Space Agency (ESA) for providing the Sentinel – 1A SAR images. The authors thank the National Aeronautics and Space Administration (NASA) for providing the DEM data. The authors thank the Shanghai Institute of Geological Survey for providing the Landform data, Quaternary strata data and faults data. The authors thank the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences (CAS) for providing the Landuse data. The authors thank the anonymous reviewers for their valuable comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was supported by the National Nature Science Funds of China; under [grant number 52038008, 52378408]; Shanghai Science and Technology Innovation Action Plan under [grant number 20DZ1202004, 22DZ1203004]; and State Grid Shanghai Electric Power Company Technology Project under [grant number 52090W220001].