A novel framework for landslide displacement prediction using MT-InSAR and machine learning techniques-Reference-Cited by-同舟云学术

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A novel framework for landslide displacement prediction using MT-InSAR and machine learning techniques

Published:2024-05 Issue: Volume:334 Page:107497
ISSN:0013-7952
Container-title:Engineering Geology
language:en
Short-container-title:Engineering Geology

Author:

Zhou Chao,Cao Ying,Gan Lulu,Wang Yue,Motagh Mahdi,Roessner Sigrid,Hu Xie,Yin Kunlong

Funder

Guangxi Key Research and Development Program

China Scholarship Council

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference72 articles.

1. InSAR-based detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: an application to Nepal[J];Bekaert;Remote Sens. Environ.,2020

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

3. Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors[J];Cao;Landslides,2016

4. Establishment of landslide groundwater level prediction model based on GA-SVM and influencing factor analysis[J];Cao;Sensors,2020

5. A simple method to help determine landslide susceptibility from spaceborne InSAR data: the Montescaglioso case study[J];Carla;Environ. Earth Sci.,2016

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2. Near-surface soil hydrothermal response feedbacks landslide activity and mechanism;Engineering Geology;2024-11

3. High-resolution landslide mapping and susceptibility assessment: Landslide temporal variations and vegetation recovery;Advances in Space Research;2024-10

4. Improving ML-based landslide susceptibility using ensemble method for sample selection: a case study of Kangra district in Himachal Pradesh, India;Environmental Science and Pollution Research;2024-09-02

5. InSAR-Driven Dynamic Landslide Hazard Mapping in Highly Vegetated Area;Remote Sensing;2024-08-31

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