88 citations as recorded by crossref.

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4. Landslide Identification in Human-Modified Alpine and Canyon Area of the Niulan River Basin Based on SBAS-InSAR and Optical Images S. Yang et al. 10.3390/rs15081998
5. Space–time landslide hazard modeling via Ensemble Neural Networks A. Dahal et al. 10.5194/nhess-24-823-2024
6. Implications of Landslide Typology and Predisposing Factor Combinations for Probabilistic Landslide Susceptibility Models: A Case Study in Lajedo Parish (Flores Island, Azores—Portugal) R. Silva et al. 10.3390/geosciences8050153
7. Predicting Earthquake-Induced Landslides by Using a Stochastic Modeling Approach: A Case Study of the 2001 El Salvador Coseismic Landslides C. Mercurio et al. 10.3390/ijgi12040178
8. Space-time landslide predictive modelling L. Lombardo et al. 10.1016/j.earscirev.2020.103318
9. Regional susceptibility assessments with heterogeneous landslide information: Slope unit- vs. pixel-based approach L. Jacobs et al. 10.1016/j.geomorph.2020.107084
10. Landslide susceptibility mapping based on the reliability of landslide and non-landslide sample H. Hong et al. 10.1016/j.eswa.2023.122933
11. Mass movement susceptibility assessment of alpine infrastructure in the Salzkammergut area, Austria L. Abad et al. 10.1016/j.ijdrr.2022.103009
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14. Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan D. Golovko et al. 10.3390/rs9090943
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16. Hybrid Computational Intelligence Models for Improvement Gully Erosion Assessment A. Arabameri et al. 10.3390/rs12010140
17. Multi-Temporal Landslide Inventory-Based Statistical Susceptibility Modeling Associated With the 2017 Mw 6.5 Jiuzhaigou Earthquake, Sichuan, China L. Luo et al. 10.3389/fenvs.2022.858635
18. The influence of systematically incomplete shallow landslide inventories on statistical susceptibility models and suggestions for improvements S. Steger et al. 10.1007/s10346-017-0820-0
19. From theory to practice: optimisation of available information for landslide hazard assessment in Rome relying on official, fragmented data sources C. Esposito et al. 10.1007/s10346-023-02095-7
20. Multi-event assessment of typhoon-triggered landslide susceptibility in the Philippines J. Jones et al. 10.5194/nhess-23-1095-2023
21. Changes in climate patterns and their association to natural hazard distribution in South Tyrol (Eastern Italian Alps) R. Schlögel et al. 10.1038/s41598-020-61615-w
22. Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling – Benefits of exploring landslide data collection effects S. Steger et al. 10.1016/j.scitotenv.2021.145935
23. Comparison of Three Mixed-Effects Models for Mass Movement Susceptibility Mapping Based on Incomplete Inventory in China Y. He & Y. Zhang 10.3390/rs14236068
24. Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan M. Juliev et al. 10.1016/j.scitotenv.2018.10.431
25. Shifting from traditional landslide occurrence modeling to scenario estimation with a “glass-box” machine learning F. Caleca et al. 10.1016/j.scitotenv.2024.175277
26. Landslide Distribution and Development Characteristics in the Beiluo River Basin F. Liu et al. 10.3390/land13071038
27. Geostatistical Modeling to Capture Seismic‐Shaking Patterns From Earthquake‐Induced Landslides L. Lombardo et al. 10.1029/2019JF005056
28. Landslide susceptibility evaluation and hazard zonation techniques – a review L. Shano et al. 10.1186/s40677-020-00152-0
29. Predictive Modelling of Landslide Susceptibility in the Western Carpathian Flysch Zone M. Barančoková et al. 10.3390/land10121370
30. Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria P. Lima et al. 10.1007/s10346-021-01693-7
31. Spatial modeling of multi-hazard threat to cultural heritage sites L. Lombardo et al. 10.1016/j.enggeo.2020.105776
32. Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling A. Dahal & L. Lombardo 10.1016/j.cageo.2023.105364
33. Modeling the area of co-seismic landslides via data-driven models: The Kaikōura example M. Moreno et al. 10.1016/j.enggeo.2023.107121
34. Retrospective evaluation of landslide susceptibility maps and review of validation practice P. Fleuchaus et al. 10.1007/s12665-021-09770-9
35. Towards the Use of Land Use Legacies in Landslide Modeling: Current Challenges and Future Perspectives in an Austrian Case Study R. Knevels et al. 10.3390/land10090954
36. A New Integrated Approach for Landslide Data Balancing and Spatial Prediction Based on Generative Adversarial Networks (GAN) H. Al-Najjar et al. 10.3390/rs13194011
37. Contribution and behavioral assessment of physical and anthropogenic factors for soil erosion using integrated deep learning and game theory I. Ahmed et al. 10.1016/j.jclepro.2023.137689
38. The influence of cartographic representation on landslide susceptibility models: empirical evidence from a Brazilian UNESCO world heritage site J. Araujo Junior et al. 10.1007/s11069-024-06576-3
39. Effect of landslide spatial representation and raster resolution on the landslide susceptibility assessment S. Yang et al. 10.1007/s12665-024-11442-3
40. Surface temperature controls the pattern of post-earthquake landslide activity M. Loche et al. 10.1038/s41598-022-04992-8
41. An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland E. Bryce et al. 10.1007/s10346-024-02368-9
42. From scenario-based seismic hazard to scenario-based landslide hazard: rewinding to the past via statistical simulations L. Luo et al. 10.1007/s00477-020-01959-x
43. An ensemble neural network approach for space–time landslide predictive modelling J. Lim et al. 10.1016/j.jag.2024.104037
44. A SHAP-enhanced XGBoost model for interpretable prediction of coseismic landslides H. Wen et al. 10.1016/j.asr.2024.07.013
45. Mapping Landslide Susceptibility Over Large Regions With Limited Data J. Woodard et al. 10.1029/2022JF006810
46. Assessing the utility of regionalized rock-mass geomechanical properties in rockfall susceptibility modelling in an alpine environment G. Bajni et al. 10.1016/j.geomorph.2022.108401
47. Application of novel ensemble models to improve landslide susceptibility mapping reliability Z. Tong et al. 10.1007/s10064-023-03328-8
48. Landslide susceptibility: a statistically-based assessment on a depositional pyroclastic ramp F. Murillo-García et al. 10.1007/s11629-018-5225-6
49. 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. 10.1016/j.earscirev.2022.104125
50. Presenting logistic regression-based landslide susceptibility results L. Lombardo & P. Mai 10.1016/j.enggeo.2018.07.019
51. The performance of landslide susceptibility models critically depends on the quality of digital elevation models J. Brock et al. 10.1080/19475705.2020.1776403
52. Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey M. Loche et al. 10.3390/rs14061321
53. Evaluating the destabilization susceptibility of active rock glaciers in the French Alps M. Marcer et al. 10.5194/tc-13-141-2019
54. An open-source and QGIS-integrated physically based model for Spatial Prediction of Rainfall-Induced Shallow Landslides (SPRIn-SL) L. Raimondi et al. 10.1016/j.envsoft.2022.105587
55. Conventional data-driven landslide susceptibility models may only tell us half of the story: Potential underestimation of landslide impact areas depending on the modeling design P. Lima et al. 10.1016/j.geomorph.2023.108638
56. The importance of investigating causative factors and training data selection for accurate landslide susceptibility assessment: the case of Ain Lahcen commune (Tetouan, Northern Morocco) A. Bounab et al. 10.1080/10106049.2022.2028905
57. An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost X. Zhou et al. 10.1080/10106049.2022.2076928
58. Benchmarking data handling strategies for landslide susceptibility modeling using random forest workflows G. Samodra et al. 10.1016/j.aiig.2024.100093
59. On the estimation of landslide intensity, hazard and density via data-driven models M. Di Napoli et al. 10.1007/s11069-023-06153-0
60. Landslide size matters: A new data-driven, spatial prototype L. Lombardo et al. 10.1016/j.enggeo.2021.106288
61. Spatiotemporal landslide susceptibility mapping using machine learning models: A case study from district Hattian Bala, NW Himalaya, Pakistan A. Hammad Khaliq et al. 10.1016/j.asej.2022.101907
62. Examining the Influence of Different Inventories on Shallow Landslide Susceptibility Modeling: An Assessment Using Machine Learning and Statistical Approaches H. Dias et al. 10.3390/geosciences15030077
63. Application of induced polarization imaging across different scales to understand surface and groundwater flow at the Hofermuehle landslide A. Flores Orozco et al. 10.1016/j.catena.2022.106612
64. Data‐driven modelling of joint debris flow release susceptibility and connectivity S. Steger et al. 10.1002/esp.5421
65. Artificial neural networks applied to landslide susceptibility: The effect of sampling areas on model capacity for generalization and extrapolation S. Gameiro et al. 10.1016/j.apgeog.2021.102598
66. When Enough Is Really Enough? On the Minimum Number of Landslides to Build Reliable Susceptibility Models G. Titti et al. 10.3390/geosciences11110469
67. Uncertainties in landslide susceptibility prediction: Influence rule of different levels of errors in landslide spatial position F. Huang et al. 10.1016/j.jrmge.2024.02.001
68. Landslide susceptibility analysis based on citizen reports T. Rohan et al. 10.1002/esp.5064
69. Landslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaóca, southeastern Brazil H. Dias et al. 10.1590/2317-4889202120200105
70. Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model D. Camilo et al. 10.1016/j.envsoft.2017.08.003
71. Spatiotemporal modeling and projection framework of rainfall-induced landslide risk under climate change B. Du et al. 10.1016/j.jenvman.2024.123474
72. The influence of forest cover on landslide occurrence explored with spatio-temporal information E. Schmaltz et al. 10.1016/j.geomorph.2017.04.024
73. Landslide Susceptibility Mapping Using Novel Hybrid Model Based on Different Mapping Units T. Zhang et al. 10.1007/s12205-022-1471-9
74. Deciphering meteorological influencing factors for Alpine rockfalls: a case study in Aosta Valley G. Bajni et al. 10.1007/s10346-021-01697-3
75. Landslide Susceptibility Mapping in Brazil: A Review H. Dias et al. 10.3390/geosciences11100425
76. Space-time susceptibility modeling of hydro-morphological processes at the Chinese national scale N. Wang et al. 10.1016/j.enggeo.2022.106586
77. Landslide susceptibility mapping using physics-guided machine learning: a case study of a debris flow event in Colorado Front Range T. Pei & T. Qiu 10.1007/s11440-024-02384-y
78. The (f)utility to account for pre-failure topography in data-driven landslide susceptibility modelling S. Steger et al. 10.1016/j.geomorph.2020.107041
79. Unsupervised machine learning with different sampling strategies and topographic factors for distinguishing between landslide source and runout areas to improve landslide inventory production J. Lai et al. 10.1080/19475705.2024.2406302
80. From spatio-temporal landslide susceptibility to landslide risk forecast T. Wang et al. 10.1016/j.gsf.2023.101765
81. A grid-based physical model to analyze the stability of slope unit S. Zhang et al. 10.1016/j.geomorph.2021.107887
82. Quantifying effectiveness of trees for landslide erosion control R. Spiekermann et al. 10.1016/j.geomorph.2021.107993
83. A hazard preparedness plan for a selected stretch of hill road between Kodaikkanal and Palani E. Sujatha 10.1007/s41870-020-00580-z
84. Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method F. Huang et al. 10.1016/j.jrmge.2023.11.001
85. Towards physics-informed neural networks for landslide prediction A. Dahal & L. Lombardo 10.1016/j.enggeo.2024.107852
86. The influence of the inventory on the determination of the rainfall-induced shallow landslides susceptibility using generalized additive models M. Bordoni et al. 10.1016/j.catena.2020.104630
87. Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds S. Steger et al. 10.1016/j.gsf.2024.101822
88. Mapping landslide susceptibility using data-driven methods J. Zêzere et al. 10.1016/j.scitotenv.2017.02.188