Strategy of oversampling geotechnical parameters through geostatistical, SMOTE, and CTGAN methods for assessing susceptibility of landslide-Reference-Cited by-同舟云学术

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Strategy of oversampling geotechnical parameters through geostatistical, SMOTE, and CTGAN methods for assessing susceptibility of landslide

Published:2023-10-19 Issue:2 Volume:21 Page:291-307
ISSN:1612-510X
Container-title:Landslides
language:en
Short-container-title:Landslides

Author:

Min Dae-Hong,Kim YoungSeok,Kim Sewon,Yoon Hyung-Koo

Funder

National Research Foundation of Kore

Korea Agency for Infrastructure Technology Advancement

Publisher

Springer Science and Business Media LLC

Subject

Geotechnical Engineering and Engineering Geology

Link

https://link.springer.com/content/pdf/10.1007/s10346-023-02166-9.pdf

Reference48 articles.

1. Aaron J, McDougall S, Kowalski J, Mitchell A, Nolde N (2022) Probabilistic prediction of rock avalanche runout using a numerical model. Landslides 19(12):2853–2869

2. Abdi L, Hashemi S (2015) To combat multi-class imbalanced problems by means of over-sampling and boosting techniques. Soft Comput 19(12):3369–3385

3. Al-Najjar HH, Pradhan B (2021) Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks. Geosci Front 12(2):625–637

4. Al-Najjar HA, Pradhan B, Sarkar R, Beydoun G, Alamri A (2021) A new integrated approach for landslide data balancing and spatial prediction based on generative adversarial networks (GAN). Remote Sensing 13(19):4011

5. Al-Najjar HA, Pradhan B, Beydoun G, Sarkar R, Park HJ and Alamri A (2022) A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using time-series SAR dataset. Gondwana Research

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