Articles | Volume 22, issue 9
https://doi.org/10.5194/nhess-22-3063-2022
© 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/nhess-22-3063-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Sep 2022
Research article |
| 19 Sep 2022
| 19 Sep 2022
Estimating global landslide susceptibility and its uncertainty through ensemble modeling
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
Jean Poesen
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
Faculty of Earth Sciences and Spatial Management, Maria-Curie Skłodowska University, Lublin, Poland
Michel Bechtold
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
Matthias Vanmaercke
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
Gabriëlle J. M. De Lannoy
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
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Cited
19 citations as recorded by crossref.
- National-scale assessment of railways exposure to rapid flow-like landslides I. Marchesini et al. https://doi.org/10.1016/j.enggeo.2024.107474
- Wealth and land-cover change govern landslide fatalities on world’s mountains S. Fidan et al. https://doi.org/10.1126/sciadv.aec2739
- Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds S. Steger et al. https://doi.org/10.1016/j.gsf.2024.101822
- Beyond boundaries: AI-optimized global landslide susceptibility mapping M. Panahi et al. https://doi.org/10.1080/19475705.2025.2493222
- Frequency Ratio–Guided Optimization of Negative Sample Selection and Sample Ratio for Landslide Susceptibility Assessment: A Case Study of the Heishui River Basin, China Z. Yang et al. https://doi.org/10.3390/app16010342
- Spatial Prediction of Landslide Susceptibility Using a Deep Learning and Partition Membership Hybrid Model Q. Lin & H. Hong https://doi.org/10.1111/tgis.70255
- A review of evolving remote sensing and automated techniques in rock glacier mapping S. Tamang et al. https://doi.org/10.1016/j.earscirev.2026.105473
- Stepwise integration of analytical hierarchy process with machine learning algorithms for landslide, gully erosion and flash flood susceptibility mapping over the North-Moungo perimeter, Cameroon A. Mfondoum et al. https://doi.org/10.1186/s40677-023-00254-5
- Towards physics-informed neural networks for landslide prediction A. Dahal & L. Lombardo https://doi.org/10.1016/j.enggeo.2024.107852
- Wireless Sensor Network-Based Rockfall and Landslide Monitoring Systems: A Review M. Ragnoli et al. https://doi.org/10.3390/s23167278
- Ensemble learning for landslide susceptibility mapping: a review of machine learning and hybrid approaches H. Jiang et al. https://doi.org/10.1007/s13146-025-01221-x
- Exacerbating landslide risks under future climate change and land use scenarios: evidence from the Western Himalayas in Pakistan A. Rehman et al. https://doi.org/10.1080/19475705.2025.2577170
- Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling A. Felsberg et al. https://doi.org/10.5194/nhess-23-3805-2023
- Decoding dynamic landslide hazard processes for a massive refugee camp in Bangladesh D. Haque et al. https://doi.org/10.1016/j.envc.2025.101172
- Global Dynamic Rainfall-Induced Landslide Susceptibility Mapping Using Machine Learning B. Li et al. https://doi.org/10.3390/rs14225795
- Development of a Precipitation-Induced Moisture-Driven Landslide Threshold Atlas for the Northwestern and Northeastern Himalayas D. Monga & P. Ganguli https://doi.org/10.1061/JHYEFF.HEENG-6745
- Assessing uncertainties in landslide susceptibility predictions in a changing environment (Styrian Basin, Austria) R. Knevels et al. https://doi.org/10.5194/nhess-23-205-2023
- A Soil Moisture-Informed Seismic Landslide Model Using SMAP Satellite Data A. Farahani & M. Ghayoomi https://doi.org/10.3390/rs17152671
- Active learning framework for landslide-induced impulse wave risk assessment using GAN-enhanced kriging and adaptive cooperative PSO W. Xu et al. https://doi.org/10.1016/j.enggeo.2026.108779
19 citations as recorded by crossref.
- National-scale assessment of railways exposure to rapid flow-like landslides I. Marchesini et al. https://doi.org/10.1016/j.enggeo.2024.107474
- Wealth and land-cover change govern landslide fatalities on world’s mountains S. Fidan et al. https://doi.org/10.1126/sciadv.aec2739
- Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds S. Steger et al. https://doi.org/10.1016/j.gsf.2024.101822
- Beyond boundaries: AI-optimized global landslide susceptibility mapping M. Panahi et al. https://doi.org/10.1080/19475705.2025.2493222
- Frequency Ratio–Guided Optimization of Negative Sample Selection and Sample Ratio for Landslide Susceptibility Assessment: A Case Study of the Heishui River Basin, China Z. Yang et al. https://doi.org/10.3390/app16010342
- Spatial Prediction of Landslide Susceptibility Using a Deep Learning and Partition Membership Hybrid Model Q. Lin & H. Hong https://doi.org/10.1111/tgis.70255
- A review of evolving remote sensing and automated techniques in rock glacier mapping S. Tamang et al. https://doi.org/10.1016/j.earscirev.2026.105473
- Stepwise integration of analytical hierarchy process with machine learning algorithms for landslide, gully erosion and flash flood susceptibility mapping over the North-Moungo perimeter, Cameroon A. Mfondoum et al. https://doi.org/10.1186/s40677-023-00254-5
- Towards physics-informed neural networks for landslide prediction A. Dahal & L. Lombardo https://doi.org/10.1016/j.enggeo.2024.107852
- Wireless Sensor Network-Based Rockfall and Landslide Monitoring Systems: A Review M. Ragnoli et al. https://doi.org/10.3390/s23167278
- Ensemble learning for landslide susceptibility mapping: a review of machine learning and hybrid approaches H. Jiang et al. https://doi.org/10.1007/s13146-025-01221-x
- Exacerbating landslide risks under future climate change and land use scenarios: evidence from the Western Himalayas in Pakistan A. Rehman et al. https://doi.org/10.1080/19475705.2025.2577170
- Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling A. Felsberg et al. https://doi.org/10.5194/nhess-23-3805-2023
- Decoding dynamic landslide hazard processes for a massive refugee camp in Bangladesh D. Haque et al. https://doi.org/10.1016/j.envc.2025.101172
- Global Dynamic Rainfall-Induced Landslide Susceptibility Mapping Using Machine Learning B. Li et al. https://doi.org/10.3390/rs14225795
- Development of a Precipitation-Induced Moisture-Driven Landslide Threshold Atlas for the Northwestern and Northeastern Himalayas D. Monga & P. Ganguli https://doi.org/10.1061/JHYEFF.HEENG-6745
- Assessing uncertainties in landslide susceptibility predictions in a changing environment (Styrian Basin, Austria) R. Knevels et al. https://doi.org/10.5194/nhess-23-205-2023
- A Soil Moisture-Informed Seismic Landslide Model Using SMAP Satellite Data A. Farahani & M. Ghayoomi https://doi.org/10.3390/rs17152671
- Active learning framework for landslide-induced impulse wave risk assessment using GAN-enhanced kriging and adaptive cooperative PSO W. Xu et al. https://doi.org/10.1016/j.enggeo.2026.108779
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
In this study we assessed global landslide susceptibility at the coarse 36 km spatial resolution of global satellite soil moisture observations to prepare for a subsequent combination of the two. Specifically, we focus therefore on the susceptibility of hydrologically triggered landslides. We introduce ensemble techniques, common in, for example, meteorology but not yet in the landslide community, to retrieve reliable estimates of the total prediction uncertainty.
In this study we assessed global landslide susceptibility at the coarse 36 km spatial resolution...
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