Articles | Volume 15, issue 23
https://doi.org/10.5194/gmd-15-8765-2022
https://doi.org/10.5194/gmd-15-8765-2022
Methods for assessment of models
 | 
06 Dec 2022
Methods for assessment of models |  | 06 Dec 2022

Transfer learning for landslide susceptibility modeling using domain adaptation and case-based reasoning

Zhihao Wang, Jason Goetz, and Alexander Brenning

Viewed

Total article views: 4,181 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,101 978 102 4,181 282 127 166
  • HTML: 3,101
  • PDF: 978
  • XML: 102
  • Total: 4,181
  • Supplement: 282
  • BibTeX: 127
  • EndNote: 166
Views and downloads (calculated since 18 May 2022)
Cumulative views and downloads (calculated since 18 May 2022)

Viewed (geographical distribution)

Total article views: 4,181 (including HTML, PDF, and XML) Thereof 3,997 with geography defined and 184 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 25 Mar 2026
Download
Short summary
A lack of inventory data can be a limiting factor in developing landslide predictive models, which are crucial for supporting hazard policy and decision-making. We show how case-based reasoning and domain adaptation (transfer-learning techniques) can effectively retrieve similar landslide modeling situations for prediction in new data-scarce areas. Using cases in Italy, Austria, and Ecuador, our findings support the application of transfer learning for areas that require rapid model development.
Share