90 citations as recorded by crossref.

1. Landslide susceptibility mapping in Badakhshan province, Afghanistan: a comparative study of machine learning algorithms A. Qasimi et al. 10.1080/10106049.2023.2248082
2. GIS-Based Expert Knowledge for Landslide Susceptibility Mapping (LSM): Case of Mostaganem Coast District, West of Algeria R. Senouci et al. 10.3390/su13020630
3. 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
4. Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran A. Arabameri et al. 10.3390/rs12030475
5. Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region Y. Yi et al. 10.1016/j.catena.2020.104851
6. Global Dynamic Rainfall-Induced Landslide Susceptibility Mapping Using Machine Learning B. Li et al. 10.3390/rs14225795
7. Multi-event assessment of typhoon-triggered landslide susceptibility in the Philippines J. Jones et al. 10.5194/nhess-23-1095-2023
8. Applying rainfall threshold estimates and frequency ratio model for landslide hazard assessment in the coastal mountain setting of South Asia A. Alam et al. 10.1016/j.nhres.2023.08.002
9. 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
10. Spatial distribution characteristics of landslides in Xiluodu Reservoir Area S. Pengcheng et al. 10.1088/1755-1315/570/4/042015
11. 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
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13. Landslide susceptibility mapping using state-of-the-art machine learning ensembles B. Pham et al. 10.1080/10106049.2021.1914746
14. 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
15. Landslide integrated characteristics and susceptibility assessment in Rongxian county of Guangxi, China L. Liao et al. 10.1007/s11629-017-4804-2
16. Arable lands under the pressure of multiple land degradation processes. A global perspective R. Prăvălie et al. 10.1016/j.envres.2020.110697
17. Assessing the spatiotemporal impact of climate change on event rainfall characteristics influencing landslide occurrences based on multiple GCM projections in China Q. Lin et al. 10.1007/s10584-020-02750-1
18. Characteristics of failure area and failure mechanism of a landslide in Yingjiang County, Yunnan, China G. Shi et al. 10.1007/s10346-020-01544-x
19. Prediction of landslides by machine learning algorithms and statistical methods in Iran S. Khezri et al. 10.1007/s12665-022-10388-8
20. Exploring the rainfall data from satellites to monitor rainfall induced landslides – A case study M. Kumar Thakur et al. 10.1016/j.asr.2020.05.015
21. Fatal landslides in China from 1940 to 2020: occurrences and vulnerabilities S. Zhang et al. 10.1007/s10346-023-02034-6
22. 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
23. Spatial landslide susceptibility mapping using integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches S. Paryani et al. 10.1007/s00704-021-03695-w
24. 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
25. Unsupervised active–transfer learning for automated landslide mapping Z. Wang & A. Brenning 10.1016/j.cageo.2023.105457
26. Spatiotemporal variations of fatal landslides in Turkey T. Görüm & S. Fidan 10.1007/s10346-020-01580-7
27. Landslide susceptibility mapping based on CNN-3D algorithm with attention module embedded Z. Yang et al. 10.1007/s10064-022-02889-4
28. Assessing Global Landslide Casualty Risk Under Moderate Climate Change Based on Multiple GCM Projections X. Wang et al. 10.1007/s13753-023-00514-w
29. 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
30. Estimating global landslide susceptibility and its uncertainty through ensemble modeling A. Felsberg et al. 10.5194/nhess-22-3063-2022
31. Landslide susceptibility assessment using the certainty factor and deep neural network W. Ma et al. 10.3389/feart.2022.1091560
32. Exploring the multiple land degradation pathways across the planet R. Prăvălie 10.1016/j.earscirev.2021.103689
33. 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
34. 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
35. Application of machine learning algorithms in landslide susceptibility mapping, Kali Valley, Kumaun Himalaya, India A. Solanki et al. 10.1080/10106049.2022.2120546
36. 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
37. Sea cliff instability susceptibility considering nearby human occupation and predictive capacity assessment S. Queiroz & F. Marques 10.1016/j.enggeo.2019.03.009
38. 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
39. 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
40. 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
41. Application of AHP-ICM and AHP-EWM in Collapse Disaster Risk Mapping in Huinan County Z. Lu et al. 10.3390/ijgi12100395
42. 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
43. 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
44. National-scale assessment of railways exposure to rapid flow-like landslides I. Marchesini et al. 10.1016/j.enggeo.2024.107474
45. 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
46. 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
47. Rapidly assessing earthquake-induced landslide susceptibility on a global scale using random forest Q. He et al. 10.1016/j.geomorph.2021.107889
48. 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
49. A Scenario-Based Case Study: Using AI to Analyze Casualties from Landslides in Chittagong Metropolitan Area, Bangladesh E. Alam et al. 10.3390/su15054647
50. Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling A. Felsberg et al. 10.5194/nhess-23-3805-2023
51. Shallow Landslide Susceptibility Mapping in Sochi Ski-Jump Area Using GIS and Numerical Modelling K. Kang et al. 10.3390/ijgi8030148
52. Risk level of landslide hazard at Probolinggo district, East Java R. Saraswati et al. 10.1051/matecconf/201822903005
53. Spatial distributions and multi-factor driving mechanism of landslide in southern Liaodong Peninsula Y. Li et al. 10.3389/fevo.2023.1339265
54. 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
55. Spatio-temporal evolution of landslides along transportation corridors of Muzaffarabad, Northern Pakistan Y. Sarfraz et al. 10.1007/s12665-023-10822-5
56. Regionalization Research of Mountain-Hazards Developing Environments for the Eurasian Continent D. Cheng & C. Gao 10.3390/land11091519
57. Landslide Susceptibility Assessment for Maragheh County, Iran, Using the Logistic Regression Algorithm A. Cemiloglu et al. 10.3390/land12071397
58. 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
59. 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
60. Spatial and Temporal Analysis of Global Landslide Reporting Using a Decade of the Global Landslide Catalog C. Dandridge et al. 10.3390/su15043323
61. Predictive Modelling of Landslide Susceptibility in the Western Carpathian Flysch Zone M. Barančoková et al. 10.3390/land10121370
62. A global landslide non-susceptibility map G. Jia et al. 10.1016/j.geomorph.2021.107804
63. Temporal Variations in Landslide Distributions Following Extreme Events: Implications for Landslide Susceptibility Modeling J. Jones et al. 10.1029/2021JF006067
64. Electrical Resistivity Tomography (ERT) Investigation for Landslides: Case Study in the Hunan Province, China M. Sun et al. 10.3390/app14073007
65. Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment M. Sameen et al. 10.1016/j.catena.2019.104249
66. GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms A. Arabameri et al. 10.1007/s11629-018-5168-y
67. Seismic and geomorphic assessment for coseismic landslides zonation in tropical volcanic contexts M. Arroyo-Solórzano et al. 10.1007/s11069-022-05492-8
68. Comparison of Three Mixed-Effects Models for Mass Movement Susceptibility Mapping Based on Incomplete Inventory in China Y. He & Y. Zhang 10.3390/rs14236068
69. A Modelling Tool for Rainfall-triggered Landslide Susceptibility Mapping and Hazard Warning based on GIS and Machine Learning H. Zhou et al. 10.1088/1755-1315/783/1/012074
70. Influence of spatial heterogeneity on landslide susceptibility in the transboundary area of the Himalayas H. Sun et al. 10.1016/j.geomorph.2023.108723
71. 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
72. 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
73. Development of a statistics-based nowcasting model for earthquake-triggered landslides in Taiwan R. Chuang et al. 10.1016/j.enggeo.2021.106177
74. Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping S. Paryani et al. 10.1007/s11069-020-04067-9
75. Slope-Unit Scale Landslide Susceptibility Mapping Based on the Random Forest Model in Deep Valley Areas H. Deng et al. 10.3390/rs14174245
76. Lithium quantification based on random forest with multi-source geoinformation in Coipasa salt flats, Bolivia F. Antezana Lopez et al. 10.1016/j.jag.2023.103184
77. 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
78. A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment W. Chen et al. 10.1080/10106049.2018.1425738
79. Effects of raster resolution on real probability of landslides X. Shao et al. 10.1016/j.rsase.2020.100364
80. 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
81. 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
82. Landslide risk index map at the municipal scale for Costa Rica A. Quesada-Román 10.1016/j.ijdrr.2021.102144
83. Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China X. Shao et al. 10.5194/gmd-16-5113-2023
84. Global landslide susceptibility prediction based on the automated machine learning (AutoML) framework G. Tang et al. 10.1080/10106049.2023.2236576
85. Landslide susceptibility mapping using artificial neural network tuned by metaheuristic algorithms M. Mehrabi & H. Moayedi 10.1007/s12665-021-10098-7
86. Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping K. Pawluszek-Filipiak et al. 10.3390/app10186335
87. 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
88. The influences of the spatial extent selection for non-landslide samples on statistical-based landslide susceptibility modelling: a case study of Anhui Province in China C. Wang et al. 10.1007/s11069-022-05252-8
89. A comparative study of composite kernels for landslide susceptibility mapping: A case study in Yongxin County, China Y. Wang et al. 10.1016/j.catena.2019.104217
90. Spatial and temporal analysis of a fatal landslide inventory in China from 1950 to 2016 Q. Lin & Y. Wang 10.1007/s10346-018-1037-6