Ensemble learning landslide susceptibility assessment with optimized non-landslide samples selection-Reference-Cited by-同舟云学术

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Ensemble learning landslide susceptibility assessment with optimized non-landslide samples selection

Published:2024-07-17 Issue:1 Volume:15 Page:
ISSN:1947-5705
Container-title:Geomatics, Natural Hazards and Risk
language:en
Short-container-title:Geomatics, Natural Hazards and Risk

Author:

Lu Jiangang¹,He Yi¹²³,Zhang Lifeng¹²³,Zhang Qing¹,Gao Binghai¹,Chen Hesheng¹,Fang Yumin¹

Affiliation:

1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, China

2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou, China

3. Key Laboratory of Science and Technology in Surveying & Mapping, Gansu Province, Lanzhou, China

Funder

Young Doctoral Fund Project of Higher Education Institutions in Gansu Province

the Outstanding Youth Fund of Gansu Province

the Key and Development Project of Lanzhou Jiaotong University under Grant

Jiangxi Provincial Department of Natural Resources

the National Natural Scientific Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/19475705.2024.2378176

Reference57 articles.

1. Machine-learning based landslide susceptibility modelling with emphasis on uncertainty analysis

2. Performance evaluation of machine learning and statistical techniques for modelling landslide susceptibility with limited field data

3. Redefining landslide susceptibility under extreme rainfall events using deep learning

4. A BRIEF OVERVIEW AND INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS

5. Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models

Cited by 1 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. A Graph–Transformer Method for Landslide Susceptibility Mapping;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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