Quantifying the contribution of multiple factors to land subsidence in the Beijing Plain, China with machine learning technology-Reference-Cited by-同舟云学术

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Quantifying the contribution of multiple factors to land subsidence in the Beijing Plain, China with machine learning technology

Published:2019-06 Issue: Volume:335 Page:48-61
ISSN:0169-555X
Container-title:Geomorphology
language:en
Short-container-title:Geomorphology

Author:

Zhou Chaofan,Gong Huili,Chen Beibei,Li Xiaojuan,Li Jiwei,Wang Xu,Gao Mingliang,Si Yuan,Guo Lin,Shi Min,Duan Guangyao

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Beijing Youth Top Talent Project

National “Double-Class” Construction of University Projects

Beijing Natural Science Foundation

Fundamental Scientific Research Funds

Natural Science Foundation of Tianjin City

Publisher

Elsevier BV

Subject

Earth-Surface Processes

Reference59 articles.

1. Anonymous, 2013. Land Subsidence International Symposium held in Venice. Eos Transactions American Geophysical Union, 65 (46):1180-1180. DOI:10.1029/EO065i046p01180-02.

2. Beijing Bureau of Geology and mineral exploration and development, 2008. Beijing underground water. China Land Press, 12.

3. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms;Berardino;IEEE Trans. Geosci. Remote Sens.,2002

4. Groundwater Resources and Land Subsidence investigations in the Toluca Valley;Calderhead,2007

5. Land subsidence along the Ionian coast of SE Sicily (Italy), detection and analysis via Small Baseline Subset (SBAS) multitemporal differential SAR interferometry;Canova;Earth Surface Processes & Landforms,2012

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