Semi-supervised deep learning based on label propagation algorithm for debris flow susceptibility assessment in few-label scenarios-Reference-Cited by-同舟云学术

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Semi-supervised deep learning based on label propagation algorithm for debris flow susceptibility assessment in few-label scenarios

Published:2024-04-06 Issue:7 Volume:38 Page:2875-2890
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Wang Qingyu,Wang Changming,Tang Haozhe,Wu Di,Wang Fei

Funder

National Natural Science Foundation of China

Key Scientific Research Project of China Railway Design Corporation

Graduate InnovationFund of Jilin University

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00477-024-02719-x.pdf

Reference53 articles.

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2. Ai X, Sun BT, Chen XZ (2022) Construction of small sample seismic landslide susceptibility evaluation model based on transfer learning: A case study of jiuzhaigou earthquake. Bull Eng Geol Environ 81:21. https://doi.org/10.1007/s10064-022-02601-6

3. Arazo E, Ortego D, Albert P, O'Connor NE, McGuinness K (2020) Pseudo-labeling and confirmation bias in deep semi-supervised learning. In: 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, pp 1–8. https://doi.org/10.1109/IJCNN48605.2020.9207304

4. Azarafza M, Azarafza M, Akgun H, Atkinson PM, Derakhshani R (2021) Deep learning-based landslide susceptibility mapping. Sci Rep 11:16. https://doi.org/10.1038/s41598-021-03585-1

5. Basheer IA, Hajmeer M (2000) Artificial neural networks: Fundamentals, computing, design, and application. J Microbiol Methods 43:3–31. https://doi.org/10.1016/s0167-7012(00)00201-3

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