Articles | Volume 15, issue 23
https://doi.org/10.5194/gmd-15-8765-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-8765-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
06 Dec 2022
Methods for assessment of models |
| 06 Dec 2022
| 06 Dec 2022
Transfer learning for landslide susceptibility modeling using domain adaptation and case-based reasoning
Department of Geography, Friedrich Schiller University Jena,
Loebdergraben 32, 07743 Jena, Germany
Jason Goetz
Department of Geography, Friedrich Schiller University Jena,
Loebdergraben 32, 07743 Jena, Germany
Alexander Brenning
Department of Geography, Friedrich Schiller University Jena,
Loebdergraben 32, 07743 Jena, Germany
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Cited
20 citations as recorded by crossref.
- From spatio-temporal landslide susceptibility to landslide risk forecast T. Wang et al. https://doi.org/10.1016/j.gsf.2023.101765
- Landslide susceptibility mapping using physics-guided machine learning: a case study of a debris flow event in Colorado Front Range T. Pei & T. Qiu https://doi.org/10.1007/s11440-024-02384-y
- Spatiotemporal landslide susceptibility modeling based on integrated transfer learning Z. Tian et al. https://doi.org/10.1007/s11069-025-07690-6
- Spatial-temporal landslide susceptibility modeling in data-scarce areas: Utilizing recurrent neural networks and transfer learning Z. Tian et al. https://doi.org/10.1016/j.eswa.2025.130929
- A case-based reasoning strategy of integrating case-level and covariate-level reasoning to automatically select covariates for spatial prediction Y. Wang et al. https://doi.org/10.1080/19475683.2024.2324398
- An ensemble neural network approach for space–time landslide predictive modelling J. Lim et al. https://doi.org/10.1016/j.jag.2024.104037
- Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review A. Pugliese Viloria et al. https://doi.org/10.3390/rs16183374
- 多特征空间自适应下的公路临水区地质灾害易发性评价 Y. Su et al. https://doi.org/10.3799/dqkx.2025.140
- Enhancing landslide susceptibility predictions with XGBoost and SHAP: a data-driven explainable AI method D. Khan et al. https://doi.org/10.1080/10106049.2025.2514725
- Unsupervised active–transfer learning for automated landslide mapping Z. Wang & A. Brenning https://doi.org/10.1016/j.cageo.2023.105457
- Spatially distributed antecedent rainfall thresholds for landslide occurrence: a multitask machine learning modelling approach L. Lucchese et al. https://doi.org/10.1080/02626667.2025.2561163
- Assessing multi-hazard susceptibility to cryospheric hazards: Lesson learnt from an Alaskan example L. Elia et al. https://doi.org/10.1016/j.scitotenv.2023.165289
- Feature adaptation for landslide susceptibility assessment in “no sample” areas Y. Su et al. https://doi.org/10.1016/j.gr.2024.03.002
- Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images S. Ma et al. https://doi.org/10.3390/app14052158
- Heterogeneous transfer learning considering feature representation and environmental consistency for landslide spatial prediction Z. Zhao et al. https://doi.org/10.1080/15481603.2024.2349343
- Transfer Learning with Attributes for Improving the Landslide Spatial Prediction Performance in Sample-Scarce Area Based on Variational Autoencoder Generative Adversarial Network M. Lin et al. https://doi.org/10.3390/land12030525
- Integrating interpretable artificial neural networks, geomorphic plausibility and transfer learning for landslide susceptibility mapping: a case study of Pacitan, East Java A. Mulabbi et al. https://doi.org/10.1007/s43621-025-01959-3
- “Ensembled transfer learning approach for error reduction in landslide susceptibility mapping of the data scare region” A. Singh et al. https://doi.org/10.1038/s41598-024-76541-4
- A single framework for assessing flash flood and landslide susceptibility: an application to the Mediterranean Liguria region, Italy A. Riveros et al. https://doi.org/10.5194/nhess-26-2437-2026
- Landslide susceptibility mapping using ensemble machine learning methods: a case study in Lombardy, Northern Italy Q. Xu et al. https://doi.org/10.1080/17538947.2024.2346263
20 citations as recorded by crossref.
- From spatio-temporal landslide susceptibility to landslide risk forecast T. Wang et al. https://doi.org/10.1016/j.gsf.2023.101765
- Landslide susceptibility mapping using physics-guided machine learning: a case study of a debris flow event in Colorado Front Range T. Pei & T. Qiu https://doi.org/10.1007/s11440-024-02384-y
- Spatiotemporal landslide susceptibility modeling based on integrated transfer learning Z. Tian et al. https://doi.org/10.1007/s11069-025-07690-6
- Spatial-temporal landslide susceptibility modeling in data-scarce areas: Utilizing recurrent neural networks and transfer learning Z. Tian et al. https://doi.org/10.1016/j.eswa.2025.130929
- A case-based reasoning strategy of integrating case-level and covariate-level reasoning to automatically select covariates for spatial prediction Y. Wang et al. https://doi.org/10.1080/19475683.2024.2324398
- An ensemble neural network approach for space–time landslide predictive modelling J. Lim et al. https://doi.org/10.1016/j.jag.2024.104037
- Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review A. Pugliese Viloria et al. https://doi.org/10.3390/rs16183374
- 多特征空间自适应下的公路临水区地质灾害易发性评价 Y. Su et al. https://doi.org/10.3799/dqkx.2025.140
- Enhancing landslide susceptibility predictions with XGBoost and SHAP: a data-driven explainable AI method D. Khan et al. https://doi.org/10.1080/10106049.2025.2514725
- Unsupervised active–transfer learning for automated landslide mapping Z. Wang & A. Brenning https://doi.org/10.1016/j.cageo.2023.105457
- Spatially distributed antecedent rainfall thresholds for landslide occurrence: a multitask machine learning modelling approach L. Lucchese et al. https://doi.org/10.1080/02626667.2025.2561163
- Assessing multi-hazard susceptibility to cryospheric hazards: Lesson learnt from an Alaskan example L. Elia et al. https://doi.org/10.1016/j.scitotenv.2023.165289
- Feature adaptation for landslide susceptibility assessment in “no sample” areas Y. Su et al. https://doi.org/10.1016/j.gr.2024.03.002
- Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images S. Ma et al. https://doi.org/10.3390/app14052158
- Heterogeneous transfer learning considering feature representation and environmental consistency for landslide spatial prediction Z. Zhao et al. https://doi.org/10.1080/15481603.2024.2349343
- Transfer Learning with Attributes for Improving the Landslide Spatial Prediction Performance in Sample-Scarce Area Based on Variational Autoencoder Generative Adversarial Network M. Lin et al. https://doi.org/10.3390/land12030525
- Integrating interpretable artificial neural networks, geomorphic plausibility and transfer learning for landslide susceptibility mapping: a case study of Pacitan, East Java A. Mulabbi et al. https://doi.org/10.1007/s43621-025-01959-3
- “Ensembled transfer learning approach for error reduction in landslide susceptibility mapping of the data scare region” A. Singh et al. https://doi.org/10.1038/s41598-024-76541-4
- A single framework for assessing flash flood and landslide susceptibility: an application to the Mediterranean Liguria region, Italy A. Riveros et al. https://doi.org/10.5194/nhess-26-2437-2026
- Landslide susceptibility mapping using ensemble machine learning methods: a case study in Lombardy, Northern Italy Q. Xu et al. https://doi.org/10.1080/17538947.2024.2346263
Saved (final revised paper)
Latest update: 13 Jun 2026
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.
A lack of inventory data can be a limiting factor in developing landslide predictive models,...
