Comparative analysis of the TabNet algorithm and traditional machine learning algorithms for landslide susceptibility assessment in the Wanzhou Region of China-Reference-Cited by-同舟云学术

Comparative analysis of the TabNet algorithm and traditional machine learning algorithms for landslide susceptibility assessment in the Wanzhou Region of China

Published:2024-03-15 Issue:8 Volume:120 Page:7627-7652
ISSN:0921-030X
Container-title:Natural Hazards
language:en
Short-container-title:Nat Hazards

Author:

Yingze Song,Yingxu Song,Xin Zhang,Jie Zhou,Degang Yang^ORCID

Funder

Chongqing Municipal Key Research and Development Program of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11069-024-06521-4.pdf

Reference63 articles.

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2. Arik SÖ, Pfister T (2021) Tabnet: attentive interpretable tabular learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 6679–6687

3. Azarafza M, Azarafza M, Akgün H, Atkinson PM, Derakhshani R (2021) Deep learning-based landslide susceptibility mapping. Sci Rep 11(1):24112

4. Ba Q, Chen Y, Deng S, Yang J, Li H (2018) A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment. Earth Sci Inf 11:373–388

5. Biau G, Scornet E (2016) A random forest guided tour. Test 25:197–227

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