Refined and dynamic susceptibility assessment of landslides using InSAR and machine learning models-Reference-Cited by-同舟云学术

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Refined and dynamic susceptibility assessment of landslides using InSAR and machine learning models

Published:2024-11 Issue:6 Volume:15 Page:101890
ISSN:1674-9871
Container-title:Geoscience Frontiers
language:en
Short-container-title:Geoscience Frontiers

Author:

Wei Yingdong,Qiu Haijun^ORCID,Liu Zijing,Huangfu Wenchao,Zhu Yaru,Liu Ya,Yang Dongdong,Kamp Ulrich^ORCID

Publisher

Elsevier BV

Reference93 articles.

1. Comparative assessment of bivariate, multivariate and machine learning models for mapping flood proneness;Al-Abadi;Nat. Hazards,2019

2. Development of time-variant landslide-prediction software considering three-dimensional subsurface unsaturated flow;An;Environ. Modell. Softw.,2016

3. Landslide susceptibility evaluation and management using different machine learning methods in The Gallicash River Watershed;Arabameri;Iran. Remote Sens.,2020

4. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains Central Japan;Ayalew;Geomorphology,2005

5. Landslide susceptibility zoning in a catchment of Zagros Mountains using fuzzy logic and GIS;Baharvand;Environ. Earth Sci.,2020

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1. Remote sensing identification of shallow landslide based on improved otsu algorithm and multi feature threshold;Frontiers in Earth Science;2024-09-09

2. Identifying the Characteristics and Implications of Post-Earthquake Debris Flow Events Based on Multi-Source Remote Sensing Images;Remote Sensing;2024-09-08

3. Investigating the internal erosion behavior and microscopic mechanisms of chemically stabilized soil: an experimental study;Frontiers in Earth Science;2024-08-27

4. Editorial: Monitoring, early warning and mitigation of natural and engineered slopes–volume III;Frontiers in Earth Science;2024-08-05

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