11 citations as recorded by crossref.

1. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy) A. Trigila et al. https://doi.org/10.1016/j.geomorph.2015.06.001
2. Application of environmental variables in statistically-based landslide susceptibility mapping: A review X. Zhao et al. https://doi.org/10.3389/feart.2023.1147427
3. Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy S. Segoni et al. https://doi.org/10.3390/rs16234491
4. GIS techniques for regional-scale landslide susceptibility assessment: the Sicily (Italy) case study G. Manzo et al. https://doi.org/10.1080/13658816.2012.693614
5. Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results V. Capecchi et al. https://doi.org/10.5194/nhess-15-75-2015
6. GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks D. Tien Bui et al. https://doi.org/10.1007/s12665-016-5919-4
7. Incorporating spatial heterogeneity of environmental variables into landslide susceptibility prediction X. Zhao et al. https://doi.org/10.1080/10106049.2025.2603057
8. Assessing the importance of conditioning factor selection in landslide susceptibility for the province of Belluno (region of Veneto, northeastern Italy) S. Meena et al. https://doi.org/10.5194/nhess-22-1395-2022
9. Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues F. Catani et al. https://doi.org/10.5194/nhess-13-2815-2013
10. Detailed and large-scale cost/benefit analyses of landslide prevention vs. post-event actions G. Salbego et al. https://doi.org/10.5194/nhess-15-2461-2015
11. Machine Learning-Based Evaluation of Susceptibility to Geological Hazards in the Hengduan Mountains Region, China J. Zhao et al. https://doi.org/10.1007/s13753-022-00401-w