Unraveling the evolution of landslide susceptibility: a systematic review of 30-years of strategic themes and trends-Reference-Cited by-同舟云学术

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Unraveling the evolution of landslide susceptibility: a systematic review of 30-years of strategic themes and trends

Published:2023-09-05 Issue:1 Volume:38 Page:
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Dong Aonan¹,Dou Jie¹^ORCID,Fu Yonghu²,Zhang Ruiqi³,Xing Ke¹

Affiliation:

1. Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan, China

2. School of Humanities and Law, Jiangsu Ocean University, Lianyungang, China

3. Department of Natural Environmental Science, The University of Tokyo, Japan

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2023.2256308

Reference103 articles.

1. Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia

2. Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

3. Landslide hazard assessment: summary review and new perspectives

4. Parallelization of the TRIGRS model for rainfall-induced landslides using the message passing interface

5. A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Interferometric Synthetic Aperture Radar (InSAR)-Based Absence Sampling for Machine-Learning-Based Landslide Susceptibility Mapping: The Three Gorges Reservoir Area, China;Remote Sensing;2024-06-29

2. Landslide susceptibility mapping core-base factors and models’ performance variability: a systematic review;Natural Hazards;2024-05-29

3. Forecasting step-like landslide displacement through diverse monitoring frequencies;2024-05-10

4. A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning;Natural Hazards;2024-04-08

5. Study on the influence of input variables on the supervised machine learning model for landslide susceptibility mapping;Environmental Earth Sciences;2024-03

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