146 citations as recorded by crossref.

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3. Same area, different timeframes: unveiling the evolution of landslide patterns and their impact on susceptibility maps T. Xiao et al. https://doi.org/10.1007/s11069-026-08458-2
4. Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy) M. Cama et al. https://doi.org/10.5194/nhess-15-1785-2015
5. The effect of different mobile uses on crash frequency among young drivers: application of statistical models and clustering analysis U. Gazder et al. https://doi.org/10.1080/17457300.2022.2092872
6. 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
7. 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
8. 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
9. Landslide Susceptibility Mapping Based on Deep Learning Algorithms Using Information Value Analysis Optimization J. Ji et al. https://doi.org/10.3390/land12061125
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11. Identification of urban sectors prone to solid waste accumulation: A machine learning approach based on social indicators L. Izquierdo-Horna et al. https://doi.org/10.1016/j.compenvurbsys.2022.101834
12. Mapping the ratoon rice suitability region in China using random forest and recursive feature elimination modeling Y. Wang & Y. Li https://doi.org/10.1016/j.fcr.2023.109016
13. Areas simultaneously susceptible and (dis-)connected to debris flows in the Dolomites (Italy): regional-scale application of a novel data-driven approach F. Pitscheider et al. https://doi.org/10.1080/17445647.2024.2307549
14. Field-based landslide susceptibility assessment in a data-scarce environment: the populated areas of the Rwenzori Mountains L. Jacobs et al. https://doi.org/10.5194/nhess-18-105-2018
15. A comparative analysis of statistical landslide susceptibility mapping in the southeast region of Minas Gerais state, Brazil C. Barella et al. https://doi.org/10.1007/s10064-018-1341-3
16. Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility P. Lima et al. https://doi.org/10.1007/s11629-021-7254-9
17. Evaluation of road blockage induced by seismic landslides under 2021 MS6.4 Yangbi earthquake Y. Wu et al. https://doi.org/10.1007/s12665-023-11319-x
18. 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
19. Machine learning-based thermokarst landslide susceptibility modeling across the permafrost region on the Qinghai-Tibet Plateau G. Yin et al. https://doi.org/10.1007/s10346-021-01669-7
20. A modified Logit model for assessment and validation of debris-flow susceptibility S. Wu et al. https://doi.org/10.1007/s10064-018-1412-5
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22. Analysing the Large-Scale Debris Flow Event in July 2022 in Horlachtal, Austria Using Remote Sensing and Measurement Data J. Rom et al. https://doi.org/10.3390/geosciences13040100
23. Comparison of landslide susceptibility assessment in volcanic terrains based on a landform unit model and two multivariate logistic regression models G. Paulin et al. https://doi.org/10.1016/j.jsames.2025.105876
24. Presenting logistic regression-based landslide susceptibility results L. Lombardo & P. Mai https://doi.org/10.1016/j.enggeo.2018.07.019
25. A novel method for estimating missing values in ship principal data Y. Kim et al. https://doi.org/10.1016/j.oceaneng.2022.110979
26. 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
27. Emulation of 2D Hydrodynamic Flood Simulations at Catchment Scale Using ANN and SVR S. Mirza Alipour & J. Leal https://doi.org/10.3390/w13202858
28. 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
29. 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
30. 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
31. Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster L. Lombardo et al. https://doi.org/10.1007/s00477-018-1518-0
32. The influence of sampling on landslide susceptibility mapping using artificial neural networks S. Gameiro et al. https://doi.org/10.1080/10106049.2022.2144475
33. 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
34. 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
35. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling J. Goetz et al. https://doi.org/10.1016/j.cageo.2015.04.007
36. A Preliminary Study of Prescription for Play on Developmental Concerns M. Oo et al. https://doi.org/10.1177/00099228241258846
37. Optimizing and validating the Gravitational Process Path model for regional debris-flow runout modelling J. Goetz et al. https://doi.org/10.5194/nhess-21-2543-2021
38. Evaluation of landslide susceptibility based on SMOTE-Tomek sampling and machine learning algorithm M. Lv et al. https://doi.org/10.1371/journal.pone.0323487
39. 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
40. Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: A comparison between logistic regression and random forest D. Sun et al. https://doi.org/10.1016/j.enggeo.2020.105972
41. Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling J. Lai et al. https://doi.org/10.3390/ijgi8090397
42. 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
43. Artificial neural networks applied to landslide susceptibility: The effect of sampling areas on model capacity for generalization and extrapolation S. Gameiro et al. https://doi.org/10.1016/j.apgeog.2021.102598
44. 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
45. Application of novel ensemble models to improve landslide susceptibility mapping reliability Z. Tong et al. https://doi.org/10.1007/s10064-023-03328-8
46. 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
47. Predicting sediment deposition rate in check-dams using machine learning techniques and high-resolution DEMs C. Conoscenti et al. https://doi.org/10.1007/s12665-021-09695-3
48. Improved Estimation of Aboveground Biomass of Disturbed Grassland through Including Bare Ground and Grazing Intensity Y. Shi et al. https://doi.org/10.3390/rs13112105
49. 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
50. 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
51. 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
52. The influence of spatial patterns in rainfall on shallow landslides H. Smith et al. https://doi.org/10.1016/j.geomorph.2023.108795
53. 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
54. Integrating machine learning and physics-based slope stability through Bayesian geostatistics: An uncertainty-aware framework for landslide prediction A. Achu et al. https://doi.org/10.1016/j.gsf.2026.102402
55. 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
56. 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
57. 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
58. 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
59. 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
60. 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
61. Forest Harvest Patterns on Private Lands in the Cascade Mountains, Washington, USA C. Soulard et al. https://doi.org/10.3390/f8100383
62. GIS-based Groundwater Spring Potential Mapping Using Data Mining Boosted Regression Tree and Probabilistic Frequency Ratio Models in Iran S. Mohsen Mousavi et al. https://doi.org/10.3934/geosci.2017.1.91
63. A systematic approach and software for the analysis of point patterns on river networks W. Schwanghart et al. https://doi.org/10.1002/esp.5127
64. A comparison of raster-based travel time surfaces against vector-based network calculations as applied in the study of rural food deserts T. Mulrooney et al. https://doi.org/10.1016/j.apgeog.2016.10.006
65. 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
66. Comparing methods of landslide data acquisition and susceptibility modelling: Examples from New Zealand H. Smith et al. https://doi.org/10.1016/j.geomorph.2021.107660
67. Rheological characterization of debris flows in the western himalayas using XGBoost and laboratory data N. Pandey & N. Satyam https://doi.org/10.1007/s40808-025-02311-4
68. Transferability of regional permafrost disturbance susceptibility modelling using generalized linear and generalized additive models A. Rudy et al. https://doi.org/10.1016/j.geomorph.2016.04.011
69. 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
70. Dynamic landslide susceptibility mapping based on the PS-InSAR deformation intensity B. Jin et al. https://doi.org/10.1007/s11356-023-31688-x
71. Influence of sampling design on landslide susceptibility modeling in lithologically heterogeneous areas A. Dornik et al. https://doi.org/10.1038/s41598-022-06257-w
72. 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
73. Bahaya Longsor di Kabupaten Sukabumi berbasis Metode Weight of Evidence (WoE), Logistic Regression (LR) dan Kombinasi WoE-LR S. Kusmajaya et al. https://doi.org/10.29244/jitl.22.2.101-106
74. Exploring the effect of absence selection on landslide susceptibility models: A case study in Sicily, Italy C. Conoscenti et al. https://doi.org/10.1016/j.geomorph.2016.03.006
75. 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
76. 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
77. Analysis of land use/land cover change (LULCC) and debris flow risks in Adama district, Ethiopia, aided by numerical simulation and deep learning-based remote sensing A. Bojer et al. https://doi.org/10.1007/s00477-023-02550-w
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