Investigating landslide data balancing for susceptibility mapping using generative and machine learning models
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10346-024-02352-3.pdf
Reference53 articles.
1. Ado M, Amitab K, Maji AK, Jasińska E, Gono R, Leonowicz Z, Jasiński M (2022) Landslide susceptibility mapping using machine learning: a literature survey. Remote Sens 14(13):3029. https://doi.org/10.3390/rs14133029
2. Agrawal K, Baweja Y, Dwivedi D, Saha R, Prasad P, Agrawal S, Kapoor S, Chaturvedi P, Mali N, Kala VU (2017) A comparison of class imbalance techniques for real-world landslide predictions. In: 2017 international conference on machine learning and data science (MLDS). IEEE, pp 1–8. https://doi.org/10.1109/MLDS.2017.21
3. Akinci H, Yavuz Ozalp A (2021) Landslide susceptibility mapping and hazard assessment in Artvin (Turkey) using frequency ratio and modified information value model. Acta Geophys 69(3):725–745. https://doi.org/10.1007/s11600-021-00577-7
4. Al-Najjar HA, 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. https://doi.org/10.1016/j.gsf.2020.09.002
5. 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 Sens 13(19):4011. https://doi.org/10.3390/rs13194011
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