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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Cited articles

Ai, X., Sun, B., and Chen, X.: Construction of small sample seismic landslide susceptibility evaluation model based on transfer learning: a case study of Jiuzhaigou earthquake, B. Eng. Geol. Environ., 81, 116, https://doi.org/10.1007/s10064-022-02601-6, 2022. 
Baktashmotlagh, M., Harandi, M. T., Lovell, B. C., and Salzmann, M.: Unsupervised domain adaptation by domain invariant projection, IEEE I. Conf. Comp. Vis., 1–8 December, 769–776, https://doi.org/10.1109/ICCV.2013.100, 2013. 
Bannour, W., Maalel, A., and Ben Ghezala, H. H.: Emergency management case-based reasoning systems: a survey of recent developments, J. Exp. Theor. Artif. In., 1–24, https://doi.org/10.1080/0952813x.2021.1952654, 2021. 
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and Vaughan, J. W.: A theory of learning from different domains, Mach. Learn., 79, 151–175, https://doi.org/10.1007/s10994-009-5152-4, 2010. 
Bordoni, M., Galanti, Y., Bartelletti, C., Persichillo, M. G., Barsanti, M., Giannecchini, R., Avanzi, G. D., Cevasco, A., Brandolini, P., Galve, J. P., and Meisina, C.: The influence of the inventory on the determination of the rainfall-induced shallow landslides susceptibility using generalized additive models, Catena, 193, 104630, https://doi.org/10.1016/j.catena.2020.104630, 2020. 
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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.
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