115 citations as recorded by crossref.

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20. A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content L. Chen et al. https://doi.org/10.3390/ijgi8040174
21. An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps H. Nefeslioglu et al. https://doi.org/10.1016/j.enggeo.2008.01.004
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23. GIS-based bivariate statistical modelling for earthquake-triggered landslides susceptibility mapping related to the 2008 Wenchuan earthquake, China C. Xu et al. https://doi.org/10.1144/qjegh2012-006
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25. Earthquake-induced landslide prediction using back-propagation type artificial neural network: case study in northern Iran A. Rajabi et al. https://doi.org/10.1007/s11069-021-04963-8
26. Landslide Susceptibility Assessment Tools v1.0.0b – Project Manager Suite: a new modular toolkit for landslide susceptibility assessment J. Torizin et al. https://doi.org/10.5194/gmd-15-2791-2022
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30. An artificial intelligence-based approach to predicting seismic hillslope stability under extreme rainfall events in the vicinity of Wolsong nuclear power plant, South Korea A. Pradhan & Y. Kim https://doi.org/10.1007/s10064-021-02138-0
31. Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models C. Polykretis & C. Chalkias https://doi.org/10.1007/s11069-018-3299-7
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33. A multi-criteria GIS model for geohazard assessment in the Charvak reservoir area, Uzbekistan D. Fazilova et al. https://doi.org/10.1016/j.ejrs.2025.09.003
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36. Neural network-based model for assessing failure potential of highway slopes in the Alishan, Taiwan Area: Pre- and post-earthquake investigation H. Lin et al. https://doi.org/10.1016/j.enggeo.2008.11.007
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40. Landslide susceptibility assessment at Wadi Jawrah Basin, Jizan region, Saudi Arabia using two bivariate models in GIS A. Youssef et al. https://doi.org/10.1007/s12303-014-0065-z
41. Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park, Ohio P. Gorsevski et al. https://doi.org/10.1007/s10346-015-0587-0
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45. Investigation of Possible Landslide Precursor Activity in a Small-Scale Laboratory Experiment S. Krokidis et al. https://doi.org/10.4018/IJAGR.2018100105
46. Back analysis of landslide susceptibility zonation mapping for the 2005 Kashmir earthquake: an assessment of the reliability of susceptibility zoning maps U. Kamp et al. https://doi.org/10.1007/s11069-009-9451-7
47. Identification of concrete aggregates using K-means clustering and level set method L. Chen et al. https://doi.org/10.1016/j.istruc.2021.08.048
48. Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland K. Pawluszek & A. Borkowski https://doi.org/10.1007/s11069-016-2725-y
49. Statistical landslide susceptibility assessment in a dynamic environment: A case study for Lanzhou City, Gansu Province, NW China J. Torizin et al. https://doi.org/10.1007/s11629-017-4717-0
50. GIS based landslide susceptibility mapping of Tevankarai Ar sub-watershed, Kodaikkanal, India using binary logistic regression analysis S. Ramani et al. https://doi.org/10.1007/s11629-011-2157-9
51. Rainfall-based criteria for assessing slump rate of mountainous highway slopes: A case study of slopes along Highway 18 in Alishan, Taiwan S. Chang et al. https://doi.org/10.1016/j.enggeo.2011.01.001
52. Regional Rainfall-Induced Landslide Risk Assessment Using Susceptibility Mapping and Unexpected High-Intensity Rainfall M. Anees et al. https://doi.org/10.1051/bioconf/202413104020
53. Evaluation of prediction capability, robustness, and sensitivity in non-linear landslide susceptibility models, Guantánamo, Cuba C. Melchiorre et al. https://doi.org/10.1016/j.cageo.2010.10.004
54. Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia B. Pradhan https://doi.org/10.1007/s12665-010-0705-1
55. Assessing spatial distribution of Coffea arabica L. in Ethiopia's highlands using species distribution models and geospatial analysis methods B. Hailu et al. https://doi.org/10.1016/j.ecoinf.2017.10.001
56. Medium-scale hazard mapping for shallow landslide initiation: the Buyukkoy catchment area (Cayeli, Rize, Turkey) H. Nefeslioglu et al. https://doi.org/10.1007/s10346-011-0267-7
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58. Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea S. Lee et al. https://doi.org/10.1007/s10346-007-0088-x
59. Review of study on post-earthquake stability of soil slopes J. Chen et al. https://doi.org/10.1088/1755-1315/304/4/042007
60. Mechanism of Root Reinforcement Involved in Rainfall-Induced Shallow Landslide Mitigation: A Review D. Mahima et al. https://doi.org/10.1007/s40098-023-00836-y
61. Random Cross-Validation Produces Biased Assessment of Machine Learning Performance in Regional Landslide Susceptibility Prediction C. Kumar et al. https://doi.org/10.3390/rs17020213
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64. Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area H. Oh & B. Pradhan https://doi.org/10.1016/j.cageo.2010.10.012
65. Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea W. Hakim et al. https://doi.org/10.1016/j.jenvman.2021.114367
66. Earthquake-induced landslides susceptibility assessment: A review of the state-of-the-art X. Shao & C. Xu https://doi.org/10.1016/j.nhres.2022.03.002
67. Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models V. Nourani et al. https://doi.org/10.1007/s11069-013-0932-3
68. A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape K. Were et al. https://doi.org/10.1016/j.ecolind.2014.12.028
69. Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria Y. Achour et al. https://doi.org/10.1007/s12517-017-2980-6
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71. Settlements and slides: a large landslide case study from the Central Cordillera of the Philippines G. Hearn & J. Roy Hart https://doi.org/10.1144/qjegh2019-050
72. Coseismic Stability Assessment of a Damaged Underground Ammunition Storage Chamber Through Ambient Vibration Recordings and Numerical Modelling F. Glueer et al. https://doi.org/10.3389/feart.2021.773155
73. Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin, Asir Region, Saudi Arabia A. Youssef et al. https://doi.org/10.1007/s10064-015-0734-9
74. Landslide susceptibility mapping of Tevankarai Ar sub-watershed, Kodaikkanal taluk, India, using weighted similar choice fuzzy model E. Sujatha & V. Rajamanickam https://doi.org/10.1007/s11069-011-9763-2
75. GIS-based landslide susceptibility mapping with logistic regression, analytical hierarchy process, and combined fuzzy and support vector machine methods: a case study from Wolong Giant Panda Natural Reserve, China Q. Meng et al. https://doi.org/10.1007/s10064-015-0786-x
76. Cyclic Behaviour of Uniform Sand in Drained and Undrained Conditions at Low Confining Stress in Small-Scale Landslide Model V. Jagodnik & Ž. Arbanas https://doi.org/10.3390/su141912797
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110. Assessment of seismically-induced landslide susceptibility after the 2015 Gorkha earthquake, Nepal S. Shrestha & T. Kang https://doi.org/10.1007/s10064-017-1191-4
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