Urban ground subsidence monitoring and prediction using time-series InSAR and machine learning approaches: a case study of Tianjin, China-Reference-Cited by-同舟云学术

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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:2024-08 Issue:16 Volume:83 Page:
ISSN:1866-6280
Container-title:Environmental Earth Sciences
language:en
Short-container-title:Environ Earth Sci

Author:

Zhang Jinlai,Kou Pinglang,Tao Yuxiang,Jin Zhao,Huang Yijian,Cui Jinhu,Liang Wenli,Liu Rui

Funder

Scientific and Technological Research Program of Chongqing Municipal Education Commission

Opening fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection

Hebei Natural Science Foundation

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12665-024-11778-w.pdf

Reference44 articles.

1. Cao Q, Zhang Y, Yang L, Chen J, Hou C (2024) Unveiling the driving factors of urban land subsidence in Beijing, China. Sci Total Environ 916:170134

2. Chen Y, He Y, Zhang L, Chen Y, Pu H, Chen B, Gao L (2021) Prediction of InSAR deformation time-series using a long short-term memory neural network. Int J Remote Sens 42:6919–6942

3. Cigna F, Esquivel Ramírez R, Tapete D (2021) Accuracy of sentinel-1 PSI and SBAS InSAR displacement velocities against GNSS and geodetic leveling monitoring data. Remote Sens 13:4800

4. Dagès T, Cohen LD, Bruckstein AM (2023) A model is worth tens of thousands of examples. In: International conference on scale space and variational methods in computer vision (pp. 223–235). Springer

5. Du Q, Li G, Zhou Y, Chai M, Chen D, Qi S, Wu G (2021) Deformation monitoring in an alpine mining area in the Tianshan Mountains based on SBAS-InSAR technology. Adv Mater Sci Eng 2021:1–15

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