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

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Latest update: 24 Apr 2024
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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.