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Urban ground subsidence monitoring and prediction using time-series InSAR and machine learning approaches: a case study of Tianjin, China

Published Online: 2024-08-09

Published Print: 2024-08

Authors

Zhang, Jinlai

Kou, Pinglang

Tao, Yuxiang

Jin, Zhao

Huang, Yijian

Cui, Jinhu

Liang, Wenli

Liu, Rui
Funding

Funding for this research was provided by:

Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJQN202100624, KJQN202100624, KJQN202100624, KJQN202100624, KJQN202100624)

Opening fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (SKLGP2023K008, SKLGP2023K008, SKLGP2023K008, SKLGP2023K008)

Hebei Natural Science Foundation (D2023403055, D2023403055, D2023403055)
License Information

Text and Data Mining valid from 2024-08-01

Version of Record valid from 2024-08-01
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Article History

Received: 5 May 2024

Accepted: 27 July 2024

First Online: 9 August 2024

Declarations

:

: The authors declare no competing interests.

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