182 citations as recorded by crossref.

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10. Investigating causal factors of shallow landslides in grassland regions of Switzerland L. Zweifel et al. https://doi.org/10.5194/nhess-21-3421-2021
11. Future Forest Cover Change Scenarios with Implications for Landslide Risk: An Example from Buzau Subcarpathians, Romania Ž. Malek et al. https://doi.org/10.1007/s00267-015-0577-y
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17. Analyzing the posterior predictive capability and usability of landslide susceptibility maps: a case of Kerala, India T. Pareek et al. https://doi.org/10.1007/s10346-024-02389-4
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19. Seismic and environmental controls on slow-moving landslides: Insights from the 2008 Wenchuan Earthquake H. Lu et al. https://doi.org/10.1016/j.enggeo.2024.107865
20. Statistically-based regional landslide susceptibility assessment in the UNESCO global geopark Caminhos dos Cânions do Sul (Brazil) M. Sugiyama et al. https://doi.org/10.1007/s11069-025-07478-8
21. Effectiveness of visually analyzing LiDAR DTM derivatives for earth and debris slide inventory mapping for statistical susceptibility modeling H. Petschko et al. https://doi.org/10.1007/s10346-015-0622-1
22. Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps S. Steger et al. https://doi.org/10.1016/j.geomorph.2016.03.015
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24. An integrated approach for landslide susceptibility mapping by considering spatial correlation and fractal distribution of clustered landslide data L. Liu et al. https://doi.org/10.1007/s10346-018-01122-2
25. Combining release and runout in statistical landslide susceptibility modeling M. Mergili et al. https://doi.org/10.1007/s10346-019-01222-7
26. Landslide susceptibility and mobilization rates in the Mount Elgon region, Uganda J. Broeckx et al. https://doi.org/10.1007/s10346-018-1085-y
27. Towards the Use of Land Use Legacies in Landslide Modeling: Current Challenges and Future Perspectives in an Austrian Case Study R. Knevels et al. https://doi.org/10.3390/land10090954
28. Landslide susceptibility maps of Italy: Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory M. Loche et al. https://doi.org/10.1016/j.earscirev.2022.104125
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33. The influence of cartographic representation on landslide susceptibility models: empirical evidence from a Brazilian UNESCO world heritage site J. Araujo Junior et al. https://doi.org/10.1007/s11069-024-06576-3
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36. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques Z. Pourtaghi et al. https://doi.org/10.1016/j.ecolind.2015.12.030
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38. An ensemble landslide hazard model incorporating rainfall threshold for Mt. Umyeon, South Korea A. Pradhan et al. https://doi.org/10.1007/s10064-017-1055-y
39. Assessing uncertainties in landslide susceptibility predictions in a changing environment (Styrian Basin, Austria) R. Knevels et al. https://doi.org/10.5194/nhess-23-205-2023
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45. 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
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48. The influence of the inventory on the determination of the rainfall-induced shallow landslides susceptibility using generalized additive models M. Bordoni et al. https://doi.org/10.1016/j.catena.2020.104630
49. Landslide-risk scenario of the Costa Viola mountain ridge (Calabria, Southern Italy) O. Terranova et al. https://doi.org/10.1080/17445647.2016.1195300
50. Deciphering meteorological influencing factors for Alpine rockfalls: a case study in Aosta Valley G. Bajni et al. https://doi.org/10.1007/s10346-021-01697-3
51. A Comprehensive Comparison of Stable and Unstable Area Sampling Strategies in Large-Scale Landslide Susceptibility Models Using Machine Learning Methods M. Sinčić et al. https://doi.org/10.3390/rs16162923
52. On the nexus between landslide susceptibility and transport infrastructure – an agent-based approach M. Schlögl et al. https://doi.org/10.5194/nhess-19-201-2019
53. National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data Q. Lin et al. https://doi.org/10.1016/j.gsf.2021.101248
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57. Landslide Susceptibility Assessment Using Conditional Analysis and Rare Events Logistics Regression: A Case-Study in the Antrodoco Area (Rieti, Italy) V. Chiessi et al. https://doi.org/10.4236/gep.2016.412001
58. Integrating Eco-DRR into landslide susceptibility assessment: The critical role of eco-environmental factors M. Broquet et al. https://doi.org/10.1016/j.jenvman.2025.127043
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63. Landslide Susceptibility Mapping Using Integrated Methods: A Case Study in the Chittagong Hilly Areas, Bangladesh Y. Rabby & Y. Li https://doi.org/10.3390/geosciences10120483
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68. Applicability of spatial prediction models for landslide susceptibility in land-use zoning instruments: a guideline in a coastal settlement in South-Central Chile P. López et al. https://doi.org/10.1080/10106049.2021.1939440
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74. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods H. Pourghasemi & M. Rossi https://doi.org/10.1007/s00704-016-1919-2
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113. Landslide hazard mapping of Ibrahim River Basin, Lebanon C. Abdallah & G. Faour https://doi.org/10.1007/s11069-016-2560-1
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