99 citations as recorded by crossref.

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24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
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31. A Scenario-Based Case Study: Using AI to Analyze Casualties from Landslides in Chittagong Metropolitan Area, Bangladesh E. Alam et al. 10.3390/su15054647
32. Risk level of landslide hazard at Probolinggo district, East Java R. Saraswati et al. 10.1051/matecconf/201822903005
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38. 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
39. Spatial and Temporal Analysis of Global Landslide Reporting Using a Decade of the Global Landslide Catalog C. Dandridge et al. 10.3390/su15043323
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46. 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
47. 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
48. Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping S. Paryani et al. 10.1007/s11069-020-04067-9
49. 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
50. 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
51. Effects of raster resolution on real probability of landslides X. Shao et al. 10.1016/j.rsase.2020.100364
52. 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
53. Landslide susceptibility mapping using artificial neural network tuned by metaheuristic algorithms M. Mehrabi & H. Moayedi 10.1007/s12665-021-10098-7
54. Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning H. He et al. 10.3390/su16093828
55. Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping K. Pawluszek-Filipiak et al. 10.3390/app10186335
56. 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
57. 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
58. Landslide susceptibility mapping in Badakhshan province, Afghanistan: a comparative study of machine learning algorithms A. Qasimi et al. 10.1080/10106049.2023.2248082
59. 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
60. Landslide susceptibility mapping using state-of-the-art machine learning ensembles B. Pham et al. 10.1080/10106049.2021.1914746
61. 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
62. 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
63. Landslide integrated characteristics and susceptibility assessment in Rongxian county of Guangxi, China L. Liao et al. 10.1007/s11629-017-4804-2
64. Arable lands under the pressure of multiple land degradation processes. A global perspective R. Prăvălie et al. 10.1016/j.envres.2020.110697
65. Prediction of landslides by machine learning algorithms and statistical methods in Iran S. Khezri et al. 10.1007/s12665-022-10388-8
66. 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
67. Spatiotemporal variations of fatal landslides in Turkey T. Görüm & S. Fidan 10.1007/s10346-020-01580-7
68. Landslide susceptibility mapping based on CNN-3D algorithm with attention module embedded Z. Yang et al. 10.1007/s10064-022-02889-4
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71. 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
72. Application of machine learning algorithms in landslide susceptibility mapping, Kali Valley, Kumaun Himalaya, India A. Solanki et al. 10.1080/10106049.2022.2120546
73. 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
74. Sea cliff instability susceptibility considering nearby human occupation and predictive capacity assessment S. Queiroz & F. Marques 10.1016/j.enggeo.2019.03.009
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76. Application of AHP-ICM and AHP-EWM in Collapse Disaster Risk Mapping in Huinan County Z. Lu et al. 10.3390/ijgi12100395
77. National-scale assessment of railways exposure to rapid flow-like landslides I. Marchesini et al. 10.1016/j.enggeo.2024.107474
78. 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
79. Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling A. Felsberg et al. 10.5194/nhess-23-3805-2023
80. A global-scale applicable framework of landslide dam formation susceptibility H. Wu et al. 10.1007/s10346-024-02306-9
81. Shallow Landslide Susceptibility Mapping in Sochi Ski-Jump Area Using GIS and Numerical Modelling K. Kang et al. 10.3390/ijgi8030148
82. 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
83. Landslide Susceptibility Assessment for Maragheh County, Iran, Using the Logistic Regression Algorithm A. Cemiloglu et al. 10.3390/land12071397
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85. 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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88. Development of a statistics-based nowcasting model for earthquake-triggered landslides in Taiwan R. Chuang et al. 10.1016/j.enggeo.2021.106177
89. 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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94. Landslide risk index map at the municipal scale for Costa Rica A. Quesada-Román 10.1016/j.ijdrr.2021.102144
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