A hybrid model to overcome landslide inventory incompleteness issue for landslide susceptibility prediction-Reference-Cited by-同舟云学术

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A hybrid model to overcome landslide inventory incompleteness issue for landslide susceptibility prediction

Published:2024-01 Issue:1 Volume:39 Page:
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Tan Jiayao¹,Yang Chi¹,Wang Yuzhou²³,Xiong Hanxiang¹,Ma Chuanming¹

Affiliation:

1. School of Environmental Studies, China University of Geosciences, Wuhan, China

2. School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

3. Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo, China

Funder

Fundamental Research Funds for the Central University, China University of Geosciences

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2024.2322066

Reference102 articles.

1. Landslide susceptibility modelling using hybrid bivariate statistical-based machine-learning method in a highland segment of Southern Western Ghats, India

2. Landslide hazard assessment: summary review and new perspectives

3. Landslide hazard assessment in the Himalayas (Nepal and Bhutan) based on Earth-Observation data

4. Landslide susceptibility mapping using GIS and weighted overlay method: a case study from North Jordan

5. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan

Cited by 2 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. Sample size effects on landslide susceptibility models: A comparative study of heuristic, statistical, machine learning, deep learning and ensemble learning models with SHAP analysis;Computers & Geosciences;2024-09

2. Landslide Susceptibility Assessment and Future Prediction with Land Use Change and Urbanization towards Sustainable Development: The Case of the Li River Valley in Yongding, China;Sustainability;2024-05-23

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