105 citations as recorded by crossref.

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2. Zonificación de la susceptibilidad a los deslizamientos en la Cordillera de Guaniguanico, Cuba. Un aporte al ordenamiento del territorio C. Cueto Gil et al. 10.5209/aguc.64675
3. Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran A. Arabameri et al. 10.3390/rs12030475
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8. A critical review on landslide susceptibility zonation: recent trends, techniques, and practices in Indian Himalaya S. Das et al. 10.1007/s11069-022-05554-x
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11. Failure characteristics and instability mechanism of the Hengshanbei catastrophic high-locality landslide in Guangdong, China J. Wang et al. 10.1007/s11069-025-07158-7
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15. Fatal landslides in China from 1940 to 2020: occurrences and vulnerabilities S. Zhang et al. 10.1007/s10346-023-02034-6
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17. Susceptibility assessment of earthquake-induced landslide by using back-propagation neural network in the Southwest mountainous area of China Y. Zhang et al. 10.1007/s10064-024-03687-w
18. Spatial analysis and hazard assessment of large-scale ancient landslides around the reservoir area of Wudongde hydropower station, China X. Shao et al. 10.1007/s11069-023-06201-9
19. Unsupervised active–transfer learning for automated landslide mapping Z. Wang & A. Brenning 10.1016/j.cageo.2023.105457
20. Assessing Global Landslide Casualty Risk Under Moderate Climate Change Based on Multiple GCM Projections X. Wang et al. 10.1007/s13753-023-00514-w
21. Modelling antecedent soil hydrological conditions to improve the prediction of landslide susceptibility in typhoon-prone regions C. Abancó et al. 10.1007/s10346-024-02242-8
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23. Landslide susceptibility assessment using the certainty factor and deep neural network W. Ma et al. 10.3389/feart.2022.1091560
24. Large landslides and deep-seated gravitational slope deformations in the Czech Flysch Carpathians: New LiDAR-based inventory T. Pánek et al. 10.1016/j.geomorph.2019.106852
25. Risk assessment of landslide casualty under incomplete information——Tienshan and Kunlun Mountainous regions of Central Asia X. Chen et al. 10.1016/j.ijdrr.2024.105057
26. A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020 J. Huang et al. 10.1007/s11356-022-23732-z
27. Development of the artificial neural network’s swarm-based approaches predicting East Azerbaijan landslide susceptibility mapping Y. Sun et al. 10.1007/s10668-023-04117-9
28. Landslide susceptibility mapping using GIS-based machine learning algorithms for the Northeast Chongqing Area, China Z. Bai et al. 10.1007/s12517-021-08871-w
29. Application of geographically weighted principal component analysis and fuzzy approach for unsupervised landslide susceptibility mapping on Gish River Basin, India T. Basu et al. 10.1080/10106049.2020.1778105
30. Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms P. Sajadi et al. 10.1186/s40562-022-00218-x
31. Rapidly assessing earthquake-induced landslide susceptibility on a global scale using random forest Q. He et al. 10.1016/j.geomorph.2021.107889
32. A Scenario-Based Case Study: Using AI to Analyze Casualties from Landslides in Chittagong Metropolitan Area, Bangladesh E. Alam et al. 10.3390/su15054647
33. Risk level of landslide hazard at Probolinggo district, East Java R. Saraswati et al. 10.1051/matecconf/201822903005
34. Spatial distributions and multi-factor driving mechanism of landslide in southern Liaodong Peninsula Y. Li et al. 10.3389/fevo.2023.1339265
35. Review on the progress and future prospects of geological disasters prediction in the era of artificial intelligence X. Zhang et al. 10.1007/s11069-024-06673-3
36. Spatio-temporal evolution of landslides along transportation corridors of Muzaffarabad, Northern Pakistan Y. Sarfraz et al. 10.1007/s12665-023-10822-5
37. Regionalization Research of Mountain-Hazards Developing Environments for the Eurasian Continent D. Cheng & C. Gao 10.3390/land11091519
38. Application of GIS-based data-driven bivariate statistical models for landslide prediction: a case study of highly affected landslide prone areas of Teesta River basin I. Poddar & R. Roy 10.1016/j.qsa.2023.100150
39. Comparison of multiple conventional and unconventional machine learning models for landslide susceptibility mapping of Northern part of Pakistan B. Aslam et al. 10.1007/s10668-022-02314-6
40. Spatial and Temporal Analysis of Global Landslide Reporting Using a Decade of the Global Landslide Catalog C. Dandridge et al. 10.3390/su15043323
41. Predictive Modelling of Landslide Susceptibility in the Western Carpathian Flysch Zone M. Barančoková et al. 10.3390/land10121370
42. A global landslide non-susceptibility map G. Jia et al. 10.1016/j.geomorph.2021.107804
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44. Electrical Resistivity Tomography (ERT) Investigation for Landslides: Case Study in the Hunan Province, China M. Sun et al. 10.3390/app14073007
45. Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment M. Sameen et al. 10.1016/j.catena.2019.104249
46. Influence of spatial heterogeneity on landslide susceptibility in the transboundary area of the Himalayas H. Sun et al. 10.1016/j.geomorph.2023.108723
47. The added value of a regional landslide susceptibility assessment: The western branch of the East African Rift A. Depicker et al. 10.1016/j.geomorph.2019.106886
48. Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble H. Hong et al. 10.1016/j.scitotenv.2020.137231
49. Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping S. Paryani et al. 10.1007/s11069-020-04067-9
50. Geospatial assessment and integrated multi-model approach for landslide susceptibility mapping in Meghalaya, India N. Badavath & S. Sahoo 10.1016/j.asr.2024.11.052
51. Combining logistic regression-based hybrid optimized machine learning algorithms with sensitivity analysis to achieve robust landslide susceptibility mapping S. Alqadhi et al. 10.1080/10106049.2021.2022009
52. Effects of raster resolution on real probability of landslides X. Shao et al. 10.1016/j.rsase.2020.100364
53. Introducing a geospatial database and GIS techniques as a decision-making tool for multicriteria decision analysis methods in landslides susceptibility assessment C. Nefros & C. Loupasakis 10.12681/bgsg.29038
54. Landslide Susceptibility Mapping Considering Landslide Spatial Aggregation Using the Dual-Frequency Ratio Method: A Case Study on the Middle Reaches of the Tarim River Basin X. Yi et al. 10.3390/rs17030381
55. Empirical and machine learning-based approaches to identify rainfall thresholds for landslide prediction: a case study of Kerala, India V. Menon & S. Kolathayar 10.1007/s42452-025-06636-8
56. Landslide susceptibility mapping using artificial neural network tuned by metaheuristic algorithms M. Mehrabi & H. Moayedi 10.1007/s12665-021-10098-7
57. Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning H. He et al. 10.3390/su16093828
58. Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping K. Pawluszek-Filipiak et al. 10.3390/app10186335
59. GIS-based landslide susceptibility assessment using statistical models: a case study from Souk Ahras province, N-E Algeria F. Mahdadi et al. 10.1007/s12517-018-3770-5
60. Optimizing landslide susceptibility mapping using integrated forest by penalizing attributes model with ensemble algorithms W. Chen et al. 10.1007/s12145-025-01727-x
61. Exploiting the land use to predict shallow landslide susceptibility: A probabilistic implementation of LAPSUS-LS A. Giarola et al. 10.1016/j.catena.2024.108437
62. Landslide susceptibility mapping in Badakhshan province, Afghanistan: a comparative study of machine learning algorithms A. Qasimi et al. 10.1080/10106049.2023.2248082
63. Performance Evaluation of Long NDVI Timeseries from AVHRR, MODIS and Landsat Sensors over Landslide-Prone Locations in Qinghai-Tibetan Plateau P. Sajadi et al. 10.3390/rs13163172
64. Landslide susceptibility mapping using state-of-the-art machine learning ensembles B. Pham et al. 10.1080/10106049.2021.1914746
65. National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data Q. Lin et al. 10.1016/j.gsf.2021.101248
66. Evaluation of Mass Movement Hazard in the Shoreline of the Intertidal Complex of El Grove (Pontevedra, Galicia) J. Valencia Ortiz et al. 10.3390/rs16132478
67. Landslide integrated characteristics and susceptibility assessment in Rongxian county of Guangxi, China L. Liao et al. 10.1007/s11629-017-4804-2
68. Arable lands under the pressure of multiple land degradation processes. A global perspective R. Prăvălie et al. 10.1016/j.envres.2020.110697
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70. Comparative study of decision tree and its advance ensemble techniques for landslide susceptibility map V. Saxena et al. 10.1007/s12040-024-02453-6
71. Evaluating causative factors for landslide susceptibility along the Imphal-Jiribam railway corridor in the North-Eastern part of India using a GIS-based statistical approach A. Singh et al. 10.1007/s11356-023-28966-z
72. Spatiotemporal variations of fatal landslides in Turkey T. Görüm & S. Fidan 10.1007/s10346-020-01580-7
73. Landslide susceptibility mapping based on CNN-3D algorithm with attention module embedded Z. Yang et al. 10.1007/s10064-022-02889-4
74. Exploring the multiple land degradation pathways across the planet R. Prăvălie 10.1016/j.earscirev.2021.103689
75. Landslide susceptibility mapping by integrating analytical hierarchy process, frequency ratio, and fuzzy gamma operator models, case study: North of Lorestan Province, Iran N. Eitvandi et al. 10.1007/s10661-022-10206-5
76. An Ensemble Approach of Feature Selection and Machine Learning Models for Regional Landslide Susceptibility Mapping in the Arid Mountainous Terrain of Southern Peru C. Kumar et al. 10.3390/rs15051376
77. Random Cross-Validation Produces Biased Assessment of Machine Learning Performance in Regional Landslide Susceptibility Prediction C. Kumar et al. 10.3390/rs17020213
78. Application of machine learning algorithms in landslide susceptibility mapping, Kali Valley, Kumaun Himalaya, India A. Solanki et al. 10.1080/10106049.2022.2120546
79. Effects of sampling intensity and non-slide/slide sample ratio on the occurrence probability of coseismic landslides X. Shao et al. 10.1016/j.geomorph.2020.107222
80. Sea cliff instability susceptibility considering nearby human occupation and predictive capacity assessment S. Queiroz & F. Marques 10.1016/j.enggeo.2019.03.009
81. Geo-spatial approach-based landslide susceptibility mapping using analytical hierarchical process, frequency ratio, logistic regression and their ensemble methods A. Saha et al. 10.1007/s42452-020-03441-3
82. Application of AHP-ICM and AHP-EWM in Collapse Disaster Risk Mapping in Huinan County Z. Lu et al. 10.3390/ijgi12100395
83. National-scale assessment of railways exposure to rapid flow-like landslides I. Marchesini et al. 10.1016/j.enggeo.2024.107474
84. Landslide Susceptibility Research Combining Qualitative Analysis and Quantitative Evaluation: A Case Study of Yunyang County in Chongqing, China W. Zhang et al. 10.3390/f13071055
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86. A global-scale applicable framework of landslide dam formation susceptibility H. Wu et al. 10.1007/s10346-024-02306-9
87. Shallow Landslide Susceptibility Mapping in Sochi Ski-Jump Area Using GIS and Numerical Modelling K. Kang et al. 10.3390/ijgi8030148
88. An Investigation of Landslide Susceptibility Using Logistic Regression and Statistical Index Methods in Dailekh District, Nepal D. Rai et al. 10.1007/s11769-022-1304-2
89. Landslide Susceptibility Assessment for Maragheh County, Iran, Using the Logistic Regression Algorithm A. Cemiloglu et al. 10.3390/land12071397
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91. Seismic and geomorphic assessment for coseismic landslides zonation in tropical volcanic contexts M. Arroyo-Solórzano et al. 10.1007/s11069-022-05492-8
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95. Slope-Unit Scale Landslide Susceptibility Mapping Based on the Random Forest Model in Deep Valley Areas H. Deng et al. 10.3390/rs14174245
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99. 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
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