2024
DOI: 10.21203/rs.3.rs-4370214/v1
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

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

Jinlai Zhang,
Pinglang Kou,
yuxiang tao
et al.

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… Show more

Help me understand this report
View published versions

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 37 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?