基于遥感解译的盐湖地区输电线路杆塔地面沉降易发性评价-Reference-Cited by-同舟云学术

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基于遥感解译的盐湖地区输电线路杆塔地面沉降易发性评价

Published:2024 Issue:2 Volume:49 Page:538
ISSN:1000-2383
Container-title:Earth Science-Journal of China University of Geosciences
language:
Short-container-title:Eart. Scie.

Author:

Jin Bijing,Yin Kunlong,Gui Lei,Zhao Binbin,Guo Baorui,Zeng Taorui

Publisher

Editorial Office of Earth Science

Reference45 articles.

1. Atkinson, P. M., Massari, R., 2011. Autologistic Modelling of Susceptibility to Landsliding in the Central Apennines, Italy. Geomorphology, 130(1/2): 55-64. https://doi.org/10.1016/j.geomorph.2011.02.001

2. Bianchini, Solari, Soldato, et al., 2019. Ground Subsidence Susceptibility (GSS) Mapping in Grosseto Plain (Tuscany, Italy) Based on Satellite InSAR Data Using Frequency Ratio and Fuzzy Logic. Remote Sensing, 11(17): 2015. https://doi.org/10.3390/rs11172015

3. Buscema, M., 2002. A Brief Overview and Introduction to Artificial Neural Networks. Substance Use & Misuse, 37(8/9/10): 1093-1148. https://doi.org/10.1081/ja-120004171

4. Chen, G., Zhang, Y., Zeng, R. Q., et al., 2018. Detection of Land Subsidence Associated with Land Creation and Rapid Urbanization in the Chinese Loess Plateau Using Time Series InSAR: A Case Study of Lanzhou New District. Remote Sensing, 10(2): 270. https://doi.org/10.3390/rs10020270

5. Du, Q., Li, G., Zhou, Y., et al., 2021. Deformation Monitoring in an Alpine Mining Area in the Tianshan Mountains Based on SBAS-InSAR Technology. Advances in Materials Science and Engineering, 2021(12): 1-15. https://doi.org/10.1155/2021/9988017

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1. 基于时序InSAR与机器学习的大范围地面沉降预测方法;Earth Science-Journal of China University of Geosciences;2024

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