48 citations as recorded by crossref.

1. Quantifying location error to define uncertainty in volcanic mass flow hazard simulations S. Mead et al. 10.5194/nhess-21-2447-2021
2. Bayesian analysis of the impact of rainfall data product on simulated slope failure for North Carolina locations S. Yatheendradas et al. 10.1007/s10596-018-9804-y
3. Surface temperature controls the pattern of post-earthquake landslide activity M. Loche et al. 10.1038/s41598-022-04992-8
4. Probabilistic analysis of rainfall-induced shallow landslide susceptibility using a physically based model and the bootstrap method I. Hwang et al. 10.1007/s10346-022-02014-2
5. Evaluation of Hydrological Rainfall Loss Methods Using Small-Scale Physical Landslide Model N. Bezak et al. 10.3390/w14172726
6. Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology E. Canli et al. 10.5194/nhess-18-2183-2018
7. An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping O. Rahmati et al. 10.3390/rs11111375
8. Rockfall Modelling in Forested Areas: The Role of Digital Terrain Model Grid Cell Size B. Žabota et al. 10.3390/app11041461
9. 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. 10.1007/s10346-018-01125-z
10. Numerical Modelling and Sensitivity Analysis of the Pitztal Valley Debris Flow Event M. Naqvi et al. 10.3390/geosciences13120378
11. Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability M. Di Napoli et al. 10.1007/s10346-020-01392-9
12. Sensitivity analysis and calibration of a dynamic physically based slope stability model T. Zieher et al. 10.5194/nhess-17-971-2017
13. A New Methodology for Mapping Past Rockfall Events: From Mobile Crowdsourcing to Rockfall Simulation Validation B. Žabota & M. Kobal 10.3390/ijgi9090514
14. Landslide susceptible areas identification using IDW and Ordinary Kriging interpolation techniques from hard soil depth at middle western Central Java, Indonesia . Yanto et al. 10.1007/s11069-021-04982-5
15. Reconstruction and Visualization of Landslide Events Based on Pre- and Post-Disaster Remote Sensing Data Z. Luo et al. 10.3390/w15112023
16. Quantifying the three-dimensional effects of anisotropic soil horizons on hillslope hydrology and stability G. Formetta & G. Capparelli 10.1016/j.jhydrol.2018.12.064
17. Physical modeling investigation of rainfall infiltration in steep layered volcanoclastic slopes G. Capparelli et al. 10.1016/j.jhydrol.2019.124199
18. Comparison of two 2-D numerical models for snow avalanche simulation M. Martini et al. 10.1016/j.scitotenv.2023.165221
19. 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. 10.1016/j.enggeo.2019.105440
20. Regional Analyses of Rainfall-Induced Landslide Initiation in Upper Gudbrandsdalen (South-Eastern Norway) Using TRIGRS Model L. Schilirò et al. 10.3390/geosciences11010035
21. An Instrumented Flume for Infiltration Process Modeling, Landslide Triggering and Propagation G. Spolverino et al. 10.3390/geosciences9030108
22. An overview of different models used in the deterministic approach for landslide susceptibility mapping W. Manan et al. 10.1051/e3sconf/202346419003
23. Shallow Landslide Susceptibility Models Based on Artificial Neural Networks Considering the Factor Selection Method and Various Non-Linear Activation Functions D. Lee et al. 10.3390/rs12071194
24. The importance of unsaturated soil properties in the development of slope susceptibility map for Old Alluvium in Singapore H. Rahardjo et al. 10.1051/e3sconf/202338206009
25. Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions O. Rahmati et al. 10.3390/rs11242995
26. Hydrological control of soil thickness spatial variability on the initiation of rainfall-induced shallow landslides using a three-dimensional model R. Tufano et al. 10.1007/s10346-021-01681-x
27. Relationship between landslide size and rainfall conditions in Taiwan C. Chen et al. 10.1007/s10346-016-0790-7
28. How robust are landslide susceptibility estimates? U. Ozturk et al. 10.1007/s10346-020-01485-5
29. Application of machine learning algorithms in landslide susceptibility mapping, Kali Valley, Kumaun Himalaya, India A. Solanki et al. 10.1080/10106049.2022.2120546
30. Temporal changes in the debris flow threshold under the effects of ground freezing and sediment storage on Mt. Fuji F. Imaizumi et al. 10.5194/esurf-9-1381-2021
31. Rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions E. Leonarduzzi et al. 10.5194/hess-25-5937-2021
32. Application of hydrological modelling for temporal prediction of rainfall-induced shallow landslides N. Bezak et al. 10.1007/s10346-019-01169-9
33. CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment): a new model for geo-hydrological hazard assessment at the basin scale A. Abbate et al. 10.5194/nhess-24-501-2024
34. Modeling windthrow effects on water runoff and hillslope stability in a mountain catchment affected by the VAIA storm L. Mauri & P. Tarolli 10.1016/j.scitotenv.2023.164831
35. 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. 10.3390/drones7020104
36. Effect of spatial resolution and data splitting on landslide susceptibility mapping using different machine learning algorithms M. Abraham et al. 10.1080/19475705.2021.2011791
37. Space–time landslide hazard modeling via Ensemble Neural Networks A. Dahal et al. 10.5194/nhess-24-823-2024
38. A hydroclimatological approach to predicting regional landslide probability using Landlab R. Strauch et al. 10.5194/esurf-6-49-2018
39. Spatial transferability of the physically based model TRIGRS using parameter ensembles L. de Vugt et al. 10.1002/esp.5770
40. On the Use of MATLAB to Import and Manipulate Geographic Data: A Tool for Landslide Susceptibility Assessment M. Gatto et al. 10.3390/geographies2020022
41. Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam H. Nguyen et al. 10.1002/gj.4885
42. 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. 10.3390/geosciences12070255
43. r.avaflow v1, an advanced open-source computational framework for the propagation and interaction of two-phase mass flows M. Mergili et al. 10.5194/gmd-10-553-2017
44. 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. 10.5194/nhess-20-505-2020
45. Computational experiments on the 1962 and 1970 landslide events at Huascarán (Peru) with r.avaflow: Lessons learned for predictive mass flow simulations M. Mergili et al. 10.1016/j.geomorph.2018.08.032
46. How well can we simulate complex hydro‐geomorphic process chains? The 2012 multi‐lake outburst flood in the Santa Cruz Valley (Cordillera Blanca, Perú) M. Mergili et al. 10.1002/esp.4318
47. Does parameterization influence the performance of slope stability model results? A case study in Rio de Janeiro, Brazil C. de Lima Neves Seefelder et al. 10.1007/s10346-016-0783-6
48. Comparing the performance of TRIGRS and TiVaSS in spatial and temporal prediction of rainfall-induced shallow landslides T. Tran et al. 10.1007/s12665-017-6635-4