63 citations as recorded by crossref.

1. Quantifying location error to define uncertainty in volcanic mass flow hazard simulations S. Mead et al.
2. Bayesian analysis of the impact of rainfall data product on simulated slope failure for North Carolina locations S. Yatheendradas et al.
3. Rockfalls trajectography: 3D models predictive capability assessment and coefficients calibration using optimization-based processes F. Raibaut et al.
4. Modelling debris flood propagation in scarce data terrain, case study in the Colombian Andes F. Cardona et al.
5. Surface temperature controls the pattern of post-earthquake landslide activity M. Loche et al.
6. Probabilistic analysis of rainfall-induced shallow landslide susceptibility using a physically based model and the bootstrap method I. Hwang et al.
7. SLEM (Shallow Landslide Express Model): A Simplified Geo-Hydrological Model for Powerlines Geo-Hazard Assessment A. Abbate & L. Mancusi
8. Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology E. Canli et al.
9. An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping O. Rahmati et al.
10. Numerical Modelling and Sensitivity Analysis of the Pitztal Valley Debris Flow Event M. Naqvi et al.
11. Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability M. Di Napoli et al.
12. Sensitivity analysis and calibration of a dynamic physically based slope stability model T. Zieher et al.
13. A New Methodology for Mapping Past Rockfall Events: From Mobile Crowdsourcing to Rockfall Simulation Validation B. Žabota & M. Kobal
14. Physical modeling investigation of rainfall infiltration in steep layered volcanoclastic slopes G. Capparelli et al.
15. GIS-Based Landslide Susceptibility Mapping with a Blended Ensemble Model and Key Influencing Factors in Sentani, Papua, Indonesia Z. Zulfahmi et al.
16. Dynamic analysis of the multi-staged ice–rock debris avalanche in the Langtang valley triggered by the 2015 Gorkha earthquake, Nepal K. Gnyawali et al.
17. Regional Analyses of Rainfall-Induced Landslide Initiation in Upper Gudbrandsdalen (South-Eastern Norway) Using TRIGRS Model L. Schilirò et al.
18. An Instrumented Flume for Infiltration Process Modeling, Landslide Triggering and Propagation G. Spolverino et al.
19. The Parraguirre ice–rock avalanche 1987, semi-arid Andes, Chile – a holistic revision J. Fürst et al.
20. An overview of different models used in the deterministic approach for landslide susceptibility mapping W. Manan et al.
21. Landslide-induced debris flows and its investigation using r.avaflow: A case study from Kotrupi, India N. Pandey et al.
22. Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions O. Rahmati et al.
23. Hydrological control of soil thickness spatial variability on the initiation of rainfall-induced shallow landslides using a three-dimensional model R. Tufano et al.
24. GIS-based Landslide Susceptibility Model for North Kerala Using Logistics Regression and Machine Learning Approaches S. Babu & R. Narayanan
25. Application of hydrological modelling for temporal prediction of rainfall-induced shallow landslides N. Bezak et al.
26. CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment): a new model for geo-hydrological hazard assessment at the basin scale A. Abbate et al.
27. Modeling windthrow effects on water runoff and hillslope stability in a mountain catchment affected by the VAIA storm L. Mauri & P. Tarolli
28. The Potential of UAV-Acquired Photogrammetric and LiDAR-Point Clouds for Obtaining Rock Dimensions as Input Parameters for Modeling Rockfall Runout Zones B. Žabota et al.
29. On the Use of MATLAB to Import and Manipulate Geographic Data: A Tool for Landslide Susceptibility Assessment M. Gatto et al.
30. r.avaflow v1, an advanced open-source computational framework for the propagation and interaction of two-phase mass flows M. Mergili et al.
31. Back calculation of the 2017 Piz Cengalo–Bondo landslide cascade with r.avaflow: what we can do and what we can learn M. Mergili et al.
32. Spatial prediction of changes in landslide susceptibility under extreme daily rainfall from the cmip6 multi-model ensemble T. Chaithong et al.
33. Integrating Physical and Machine Learning Models for Enhanced Landslide Prediction in Data-Scarce Environments H. Al-Najjar et al.
34. Evaluation of Hydrological Rainfall Loss Methods Using Small-Scale Physical Landslide Model N. Bezak et al.
35. Rockfall Modelling in Forested Areas: The Role of Digital Terrain Model Grid Cell Size B. Žabota et al.
36. Landslide susceptibility zoning through physically-based limit equilibrium method modelling F. Mevoli et al.
37. Assessment of rainfall-induced landslide susceptibility at the regional scale using a physically based model and fuzzy-based Monte Carlo simulation H. Park et al.
38. Landslide susceptible areas identification using IDW and Ordinary Kriging interpolation techniques from hard soil depth at middle western Central Java, Indonesia . Yanto et al.
39. Reconstruction and Visualization of Landslide Events Based on Pre- and Post-Disaster Remote Sensing Data Z. Luo et al.
40. Quantifying the three-dimensional effects of anisotropic soil horizons on hillslope hydrology and stability G. Formetta & G. Capparelli
41. Comparison of two 2-D numerical models for snow avalanche simulation M. Martini et al.
42. Exploring hydrological dynamics of layered pyroclastic soils by combining laboratory and field experiments with a numerical model R. Busti et al.
43. Shallow-landslide stability evaluation in loess areas according to the Revised Infinite Slope Model: a case study of the 7.25 Tianshui sliding-flow landslide events of 2013 in the southwest of the Loess Plateau, China J. Zhuang et al.
44. Shallow Landslide Susceptibility Models Based on Artificial Neural Networks Considering the Factor Selection Method and Various Non-Linear Activation Functions D. Lee et al.
45. The importance of unsaturated soil properties in the development of slope susceptibility map for Old Alluvium in Singapore H. Rahardjo et al.
46. Relationship between landslide size and rainfall conditions in Taiwan C. Chen et al.
47. How robust are landslide susceptibility estimates? U. Ozturk et al.
48. Application of machine learning algorithms in landslide susceptibility mapping, Kali Valley, Kumaun Himalaya, India A. Solanki et al.
49. Temporal changes in the debris flow threshold under the effects of ground freezing and sediment storage on Mt. Fuji F. Imaizumi et al.
50. Probabilistic back analysis for landslide susceptibility assessment in data-scarce regions using a Bayesian approach I. Hwang et al.
51. Rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions E. Leonarduzzi et al.
52. Calibration scenarios for physically based rainfall-induced landslide modelling at regional scale. Application to Vall d’Aran (Central Pyrenees, Spain) F. Asurza et al.
53. Effect of spatial resolution and data splitting on landslide susceptibility mapping using different machine learning algorithms M. Abraham et al.
54. Space–time landslide hazard modeling via Ensemble Neural Networks A. Dahal et al.
55. Evolution and critical evaluation of deterministic physically based rainfall-induced landslide susceptibility mapping: a mixed review R. Sachinthaka et al.
56. A hydroclimatological approach to predicting regional landslide probability using Landlab R. Strauch et al.
57. Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico R. Baum et al.
58. Physically based model for assessing rainfall-induced deep-seated landslides using a hydrological-geotechnical model S. Phakdimek et al.
59. Spatial transferability of the physically based model TRIGRS using parameter ensembles L. de Vugt et al.
60. Research on geological hazard characteristics and susceptibility of the Duku Highway based on SBAS-InSAR and improved spatiotemporal clustering Y. Niu et al.
61. Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam H. Nguyen et al.
62. Spatiotemporal decoupling of rainfall patterns and shallow landslide stability: Lessons learned from calibrated ensemble physically-based models M. Wu et al.
63. Challenges Assessing Rock Slope Stability Using the Strength Reduction Method with the Hoek–Brown Criterion on the Example of Vals (Tyrol/Austria) M. Illeditsch et al.