144 citations as recorded by crossref.

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5. Application and comparison of different ensemble learning machines combining with a novel sampling strategy for shallow landslide susceptibility mapping Z. Liang et al. https://doi.org/10.1007/s00477-020-01893-y
6. In Pursuit of Inclusion: Studying the Impact of EDI Policies in the Workplace and Exploring the Challenges of Measurement T. Garakani et al. https://doi.org/10.7202/1111225ar
7. Evaluating model robustness in landslide susceptibility mapping using a unified data-consistent framework in northern Thailand P. Manopkawee et al. https://doi.org/10.1038/s41598-026-50703-y
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24. A novel method for estimating missing values in ship principal data Y. Kim et al. https://doi.org/10.1016/j.oceaneng.2022.110979
25. Spatio-temporal analysis of slope-type debris flow activity in Horlachtal, Austria, based on orthophotos and lidar data since 1947 J. Rom et al. https://doi.org/10.5194/nhess-23-601-2023
26. Emulation of 2D Hydrodynamic Flood Simulations at Catchment Scale Using ANN and SVR S. Mirza Alipour & J. Leal https://doi.org/10.3390/w13202858
27. 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
28. Comparative Performance of Machine Learning Models for Landslide Susceptibility Assessment: Impact of Sampling Strategies in Highway Buffer Zone Z. Tang et al. https://doi.org/10.3390/app15158416
29. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model H. Hussin et al. https://doi.org/10.1016/j.geomorph.2015.10.030
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32. Spatial patterns and drivers of smallholder oil palm expansion within peat swamp forests of Riau, Indonesia J. Zhao et al. https://doi.org/10.1088/1748-9326/ac4dc6
33. Landslide susceptibility mapping of the Sera River Basin using logistic regression model N. Raja et al. https://doi.org/10.1007/s11069-016-2591-7
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38. The propagation of inventory-based positional errors into statistical landslide susceptibility models S. Steger et al. https://doi.org/10.5194/nhess-16-2729-2016
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41. 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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43. Sediment Yield in Dam-Controlled Watersheds in the Pisha Sandstone Region on the Northern Loess Plateau, China F. Xie et al. https://doi.org/10.3390/land10111264
44. Application of novel ensemble models to improve landslide susceptibility mapping reliability Z. Tong et al. https://doi.org/10.1007/s10064-023-03328-8
45. Development and validation of risk prediction model for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, 2005–2021 N. Gelaw et al. https://doi.org/10.1371/journal.pone.0276472
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48. The dilemma of determining the superiority of data mining models: optimal sampling balance and end users’ perspectives matter M. Teimouri & A. Kornejady https://doi.org/10.1007/s10064-019-01687-9
49. Predicting gully occurrence at watershed scale: Comparing topographic indices and multivariate statistical models C. Conoscenti & E. Rotigliano https://doi.org/10.1016/j.geomorph.2020.107123
50. Landslide susceptibility mapping using an integration of different statistical models for the 2015 Nepal earthquake in Tibet S. Huang & L. Chen https://doi.org/10.1080/19475705.2024.2396908
51. The influence of spatial patterns in rainfall on shallow landslides H. Smith et al. https://doi.org/10.1016/j.geomorph.2023.108795
52. Assessment of Thaw-Hazard susceptibility in permafrost regions using multisource data and machine learning: A case study in Qinghai Province, China G. Wei et al. https://doi.org/10.1016/j.coldregions.2025.104648
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54. A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020 J. Huang et al. https://doi.org/10.1007/s11356-022-23732-z
55. Investigating the effects of landslides inventory completeness on susceptibility mapping and frequency-area distributions: Case of Taounate province, Northern Morocco R. Sahrane et al. https://doi.org/10.1016/j.catena.2022.106737
56. Intersection of fluvial incision and weak geologic structures cause divergence from a universal threshold slope model of landslide occurrence F. Williams et al. https://doi.org/10.1016/j.geomorph.2021.107795
57. Terrain Controls and Landscape-Scale Susceptibility Modelling of Active-Layer Detachments, Sabine Peninsula, Melville Island, Nunavut A. Rudy et al. https://doi.org/10.1002/ppp.1900
58. The Effect of Observation Data Sampling Methods on Infiltration Areas by Maximum Entropy Model M. Jafarzadeh et al. https://doi.org/10.52547/jwmr.11.22.96
59. The performance of using an autoencoder for prediction and susceptibility assessment of landslides: A case study on landslides triggered by the 2018 Hokkaido Eastern Iburi earthquake in Japan K. Nam & F. Wang https://doi.org/10.1186/s40677-019-0137-5
60. Forest Harvest Patterns on Private Lands in the Cascade Mountains, Washington, USA C. Soulard et al. https://doi.org/10.3390/f8100383
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64. The influence of forest cover on landslide occurrence explored with spatio-temporal information E. Schmaltz et al. https://doi.org/10.1016/j.geomorph.2017.04.024
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68. Landslide susceptibility assessment in Qinzhou based on rough set and semi-supervised support vector machine C. Kong et al. https://doi.org/10.1007/s12145-023-01087-4
69. Dynamic landslide susceptibility mapping based on the PS-InSAR deformation intensity B. Jin et al. https://doi.org/10.1007/s11356-023-31688-x
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71. Comparing physical and statistical landslide susceptibility models at the scale of individual trees R. Spiekermann et al. https://doi.org/10.1016/j.geomorph.2023.108870
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74. LAND-SE: a software for statistically based landslide susceptibility zonation, version 1.0 M. Rossi & P. Reichenbach https://doi.org/10.5194/gmd-9-3533-2016
75. A statistical approach for sea cliff hazard and setback lines mapping in the northeast of Peniche, Portugal N. Penacho & F. Marques https://doi.org/10.1016/j.geomorph.2023.108669
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