Exploring class imbalance with under-sampling, over-sampling, and hybrid sampling based on Mahalanobis distance for landslide susceptibility assessment: a case study of the 2018 Iburi earthquake induced landslides in Hokkaido, Japan-Reference-Cited by-同舟云学术

Exploring class imbalance with under-sampling, over-sampling, and hybrid sampling based on Mahalanobis distance for landslide susceptibility assessment: a case study of the 2018 Iburi earthquake induced landslides in Hokkaido, Japan

Published:2023-11-07 Issue:1 Volume:28 Page:71-94
ISSN:1226-4806
Container-title:Geosciences Journal
language:en
Short-container-title:Geosci J

Author:

Nam Kounghoon,Kim Jongtae,Chae Byung-Gon

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

https://link.springer.com/content/pdf/10.1007/s12303-023-0033-6.pdf

Reference49 articles.

1. Ado, M., Amitab, K., Maji, A.K., Jasińska, E., Gono, R., Leonowicz, Z., and Jasiński, M., 2002, Landslide susceptibility mapping using machine learning: A literature survey. Remote Sensing, 14, 3029. https://doi.org/10.3390/rs14133029

2. Al-Najjar, H.A.H., Pradhan, B., Sarkar, R., Beydoun, G., and Alamri, A., 2021, A new integrated approach for landslide data balancing and spatial prediction based on Generative Adversarial Networks (GAN). Remote Sensing, 13, 4011. https://doi.org/10.3390/rs13194011

3. Azarafza, M., Azarafza, M., Akgün, H., Atkinson, P.M., and Derakhshani, R., 2021, Deep learning-based landslide susceptibility mapping. Scientific Reports, 11, 24112. https://doi.org/10.1038/s41598-021-03585-1

4. Bhuyan, K., Tanyaş, H., Nava, L., Puliero, S., Meena, S.R., Floris, M., van Westen, C., and Catani, F., 2023, Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data. Scientific Reports, 13, 162. https://doi.org/10.1038/s41598-022-27352-y

5. Cao, H., Xie, X., Shi, J., and Wang, Y., 2022, Evaluating the validity of class balancing algorithms-based machine learning models for geogenic contaminated groundwaters prediction. Journal of Hydrology, 610, 127933. https://doi.org/10.1016/j.jhydrol.2022.127933