30 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. Landslide susceptibility modeling in a complex mountainous region of Sikkim Himalaya using new hybrid data mining approach A. Islam et al. https://doi.org/10.1080/10106049.2021.2009920
3. Soil moisture information can improve shallow landslide forecasting using the hydrometeorological threshold approach P. Marino et al. https://doi.org/10.1007/s10346-020-01420-8
4. Integrated geostructural, seismic and infrared thermography surveys for the study of an unstable rock slope in the Peloritani Chain (NE Sicily) S. Mineo et al. https://doi.org/10.1016/j.enggeo.2015.06.010
5. GIS-based ensemble soft computing models for landslide susceptibility mapping B. Pham et al. https://doi.org/10.1016/j.asr.2020.05.016
6. Integrating Expert Knowledge with Statistical Analysis for Landslide Susceptibility Assessment at Regional Scale C. Chalkias et al. https://doi.org/10.3390/geosciences6010014
7. Improving transferability strategies for debris flow susceptibility assessment: Application to the Saponara and Itala catchments (Messina, Italy) M. Cama et al. https://doi.org/10.1016/j.geomorph.2017.03.025
8. Evaluation of deep learning algorithms for landslide susceptibility mapping in an alpine-gorge area: a case study in Jiuzhaigou County D. Wang et al. https://doi.org/10.1007/s11629-022-7326-5
9. A test of transferability for landslides susceptibility models under extreme climatic events: application to the Messina 2009 disaster L. Lombardo et al. https://doi.org/10.1007/s11069-014-1285-2
10. Assessment of landslide-triggering factors and occurrence using morphometric parameters in Geyraz Basin, Tokat, Northern Turkey T. Bayer Altın & E. Gökkaya https://doi.org/10.1007/s12665-018-7315-8
11. Landslide distribution and size in response to Quaternary fault activity: the Peloritani Range, NE Sicily, Italy F. Bucci et al. https://doi.org/10.1002/esp.3898
12. Geometric approach based tool for shallow landslides propagation assessment (ShaLPA) at basin scale L. Falconi et al. https://doi.org/10.1016/j.envsoft.2025.106512
13. Distribution Pattern of Landslides Triggered by the 2014 Ludian Earthquake of China: Implications for Regional Threshold Topography and the Seismogenic Fault Identification S. Zhou et al. https://doi.org/10.3390/ijgi5040046
14. Landslide susceptibility assessment along the Dubair-Dudishal section of the Karakoram Highway, Northwestern Himalayas, Pakistan J. Iqbal et al. https://doi.org/10.13168/AGG.2021.0010
15. Derivation and evaluation of landslide-triggering thresholds by a Monte Carlo approach D. Peres & A. Cancelliere https://doi.org/10.5194/hess-18-4913-2014
16. Debris and mud flows runout assessment: a comparison among empirical geometric equations in the Giampilieri and Briga basins (east Sicily, Italy) affected by the event of October 1, 2009 L. Falconi et al. https://doi.org/10.1007/s11069-023-05945-8
17. Modeling impacts of climate change on return period of landslide triggering D. Peres & A. Cancelliere https://doi.org/10.1016/j.jhydrol.2018.10.036
18. Comparing methods for determining landslide early warning thresholds: potential use of non-triggering rainfall for locations with scarce landslide data availability D. Peres & A. Cancelliere https://doi.org/10.1007/s10346-021-01704-7
19. Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment (Sicily, Italy) M. Cama et al. https://doi.org/10.1007/s12665-015-5047-6
20. Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy) M. Cama et al. https://doi.org/10.5194/nhess-15-1785-2015
21. A Small Landslide as a Big Lesson: Drones and GIS for Monitoring and Teaching Slope Instability B. Zaragozí et al. https://doi.org/10.3390/geosciences15100375
22. The RUSLE erosion index as a proxy indicator for debris flow susceptibility A. Zini et al. https://doi.org/10.1007/s10346-014-0515-8
23. Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy S. Segoni et al. https://doi.org/10.3390/rs16234491
24. Presence-only approach to assess landslide triggering-thickness susceptibility: a test for the Mili catchment (north-eastern Sicily, Italy) L. Lombardo et al. https://doi.org/10.1007/s11069-016-2443-5
25. Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster L. Lombardo et al. https://doi.org/10.1007/s00477-018-1518-0
26. Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messina (Sicily, southern Italy) L. Lombardo et al. https://doi.org/10.1007/s11069-015-1915-3
27. Assessment and validation of GIS-based landslide susceptibility maps: a case study from Feltrino stream basin (Central Italy) M. Sciarra et al. https://doi.org/10.1007/s10064-016-0954-7
28. Exploiting Maximum Entropy method and ASTER data for assessing debris flow and debris slide susceptibility for the Giampilieri catchment (north‐eastern Sicily, Italy) L. Lombardo et al. https://doi.org/10.1002/esp.3998
29. Landslide inventory and susceptibility modelling using geospatial tools, in Hunza-Nagar valley, northern Pakistan A. Bacha et al. https://doi.org/10.1007/s11629-017-4697-0
30. Automatic Landslide Inventory Mapping Approach Based on Change Detection Technique With Very-High-Resolution Images L. Zhiyong et al. https://doi.org/10.1109/LGRS.2020.3041409