Articles | Volume 26, issue 7
https://doi.org/10.5194/nhess-26-3253-2026
https://doi.org/10.5194/nhess-26-3253-2026
Research article
 | 
14 Jul 2026
Research article |  | 14 Jul 2026

Feature selection for landslide forecasting models in Southern Andes

Manuel Labbé, Millaray Curilem, Ivo Fustos-Toribio, and Mario Pooley

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Cited articles

Abraham, M., Pothuraju, D., and Satyam, N.: Rainfall thresholds for prediction of landslides in idukki, india: an empirical approach, Water, 11, 2113, https://doi.org/10.3390/w11102113, 2019. 
Abraham, M., Satyam, N., Kushal, S., Rosi, A., Pradhan, B., and Segoni, S.: Rainfall threshold estimation and landslide forecasting for kalimpong, india using sigma model, Water, 12, 1195, https://doi.org/10.3390/w12041195, 2020. 
Adnan, M., Rahman, M., Ahmed, N., Ahmed, B., Rabbi, M., and Rahman, R.: Improving spatial agreement in machine learning-based landslide susceptibility mapping, Remote Sens., 12, 3347, https://doi.org/10.3390/rs12203347, 2020. 
Ballabh, H., Pillay, S., Negi, G., and Pillay, K.: Relationship between selected physiographic features and landslide occurrence around four hydropower projects in bhagirathi valley of uttarakhand, western himalaya, india, Int. J. Geosci., 5, 1088–1099, https://doi.org/10.4236/ijg.2014.510093, 2014. 
Barnhart, K. R., George, D. L., Collins, A. L., Schaefer, L. N., and Staley, D. M.: Uncertainty Reduction for Subaerial Landslide‐Tsunami Hazards, J. Geophys. Res.-Earth, 130, https://doi.org/10.1029/2024jf007906, 2025. 
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Short summary
We investigated methods to improve the prediction of landslides triggered by heavy rainfall in southern Chile, utilising local soil and climate data. We tested different models and selected the most critical environmental factors. We improved the process for making forecasts in areas with limited monitoring. Our results help create faster and more reliable warnings and can guide safety planning in other mountain regions facing similar risks.
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