160 citations as recorded by crossref.

1. Landslide susceptibility assessment in scarce-data regions using remote sensing data E. Aristizábal-Giraldo & D. Ruiz-Vásquez https://doi.org/10.17533/udea.redin.20231030
2. A comparative study of various combination strategies for landslide susceptibility mapping considering landslide types L. Yu et al. https://doi.org/10.1016/j.gsf.2024.101999
3. Prioritization of Landslide Effective Factors using Logistic Regression (Case Study: A part of KopeDagh-HezarMasjedZone) S. Abdi Bastami et al. https://doi.org/10.29252/jwmr.10.19.154
4. Development of shallow landslide susceptibility maps incorporating relative spacing index for forest management H. Asada et al. https://doi.org/10.1016/j.indic.2024.100515
5. Performance evaluation for four GIS-based models purposed to predict and map landslide susceptibility: A case study at a World Heritage site in Southwest China Y. Jiao et al. https://doi.org/10.1016/j.catena.2019.104221
6. Landslide development and susceptibility along the Yunling–Yanjing segment of the Lancang River using grid and slope units H. Wen et al. https://doi.org/10.1007/s11069-024-06495-3
7. Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management? H. Pourghasemi et al. https://doi.org/10.1016/j.gsf.2019.10.008
8. Assessment of resampling methods on performance of landslide susceptibility predictions using machine learning in Kendari City, Indonesia S. Aldiansyah & F. Wardani https://doi.org/10.2166/wpt.2024.002
9. Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China X. Hu et al. https://doi.org/10.1007/s11069-020-04371-4
10. Landslide Susceptibility Assessment of Mauritius Island (Indian Ocean) V. Marsala et al. https://doi.org/10.3390/geosciences9120493
11. Landslide susceptibility hazard map in southwest Sweden using artificial neural network A. Abbaszadeh Shahri et al. https://doi.org/10.1016/j.catena.2019.104225
12. Investigating the Effects of Cell Size in Statistical Landslide Susceptibility Modelling for Different Landslide Typologies: A Test in Central–Northern Sicily C. Martinello et al. https://doi.org/10.3390/app13021145
13. Landslide Susceptibility Mapping by Comparing GIS-Based Bivariate Methods: A Focus on the Geomorphological Implication of the Statistical Results L. Coco et al. https://doi.org/10.3390/rs13214280
14. The Use of Machine Learning for Accessing Landslide Susceptibility Class: Study Case of Kecamatan Pacet, Kabupaten Mojokerto L. Irawan et al. https://doi.org/10.1088/1755-1315/884/1/012006
15. Geology, geomorphology and geohazards on a section of the Betic coastline, southern Spain G. Hearn https://doi.org/10.1144/qjegh2018-134
16. Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm T. Kavzoglu et al. https://doi.org/10.1016/j.enggeo.2015.04.004
17. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy) A. Trigila et al. https://doi.org/10.1016/j.geomorph.2015.06.001
18. Morphometric analysis of debris flows basins in the Eastern Rhodopes (Bulgaria) using geospatial technologies V. Nikolova et al. https://doi.org/10.1007/s11069-020-04301-4
19. Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messina (Sicily, southern Italy) L. Lombardo et al. https://doi.org/10.1007/s11069-015-1915-3
20. Deadly disasters in southeastern South America: flash floods and landslides of February 2022 in Petrópolis, Rio de Janeiro E. Alcântara et al. https://doi.org/10.5194/nhess-23-1157-2023
21. Exploring the geomorphological adequacy of the landslide susceptibility maps: A test for different types of landslides in the Bidente river basin (northern Italy) C. Martinello et al. https://doi.org/10.1016/j.catena.2024.107835
22. Exploring performance and robustness of shallow landslide susceptibility modeling at regional scale using different training and testing sets M. Conforti et al. https://doi.org/10.1007/s12665-023-10844-z
23. Effects of combination of influencing factors on earthquake-induced landslide susceptibility assessments Y. Zhang et al. https://doi.org/10.1007/s12665-025-12203-6
24. Beyond Generalisation: Regionally Calibrated Feature Selection with Machine Learning for Data-driven Landslide Hazard Assessment in the Himalayas M. Dwivedi et al. https://doi.org/10.1007/s41748-026-01047-0
25. Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: a case study in Sichuan, China C. Zhu et al. https://doi.org/10.1007/s11069-019-03806-x
26. Landslide susceptibility mapping using artificial intelligence models: a case study in the Himalayas M. Hussain et al. https://doi.org/10.1007/s10346-025-02466-2
27. GIS‐based susceptibility assessment of the occurrence of gully headcuts and pipe collapses in a semi‐arid environment: Golestan Province, NE Iran N. Kariminejad et al. https://doi.org/10.1002/ldr.3397
28. Selection of landslide affecting factors based on strong association analysis L. Li et al. https://doi.org/10.1088/1757-899X/780/7/072051
29. Landslide susceptibility assessment framework in the central Rif chain, Northern Morocco O. Obda et al. https://doi.org/10.1007/s42990-025-00216-z
30. Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya B. Manandhar et al. https://doi.org/10.1108/EC-07-2023-0374
31. A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt. Woomyeon, South Korea N. Vasu & S. Lee https://doi.org/10.1016/j.geomorph.2016.03.023
32. A Tool for Classification and Regression Using Random Forest Methodology: Applications to Landslide Susceptibility Mapping and Soil Thickness Modeling D. Lagomarsino et al. https://doi.org/10.1007/s10666-016-9538-y
33. Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan J. Dou et al. https://doi.org/10.1371/journal.pone.0133262
34. The (f)utility to account for pre-failure topography in data-driven landslide susceptibility modelling S. Steger et al. https://doi.org/10.1016/j.geomorph.2020.107041
35. Landslide Susceptibility Assessment in Jecheon Using a Convolutional Neural Network Based on Spatial Context E. Jo et al. https://doi.org/10.9719/EEG.2025.58.6.823
36. Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results V. Capecchi et al. https://doi.org/10.5194/nhess-15-75-2015
37. Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning R. Costache et al. https://doi.org/10.1016/j.jhydrol.2020.124808
38. 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
39. Landslide-hazard mapping through multi-technique activity assessment: an example from the Betic Cordillera (southern Spain) J. Jiménez-Perálvarez et al. https://doi.org/10.1007/s10346-017-0851-6
40. A novel fuzzy K-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS D. Tien Bui et al. https://doi.org/10.1007/s10346-016-0708-4
41. Investigation of the effects of hybrid modeling approaches, factor standardization, and categorical mapping on the performance of landslide susceptibility mapping in Van, Turkey D. Ozturk & N. Uzel-Gunini https://doi.org/10.1007/s11069-022-05480-y
42. Influence of spatial heterogeneity on landslide susceptibility in the transboundary area of the Himalayas H. Sun et al. https://doi.org/10.1016/j.geomorph.2023.108723
43. A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS Q. Nguyen et al. https://doi.org/10.3390/su9050813
44. A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment M. Abedini et al. https://doi.org/10.1080/10106049.2018.1499820
45. Geospatial assessment of landslide-prone areas in the southern part of Anambra State, Nigeria using classical statistical models V. Nwazelibe & J. Egbueri https://doi.org/10.1007/s12665-024-11533-1
46. Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size P. Tsangaratos & I. Ilia https://doi.org/10.1016/j.catena.2016.06.004
47. Innovative landslide susceptibility mapping supported by geomorphon and geographical detector methods W. Luo & C. Liu https://doi.org/10.1007/s10346-017-0893-9
48. Integration of multi-criteria decision analysis and statistical models for landslide susceptibility mapping in the western Algiers Province (Algeria) using GIS techniques and remote sensing data S. Mokadem et al. https://doi.org/10.1007/s12518-024-00548-9
49. 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
50. Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam D. Tien Bui et al. https://doi.org/10.1080/17538947.2016.1169561
51. Improvement of the predictive performance of landslide mapping models in mountainous terrains using cluster sampling M. Riaz et al. https://doi.org/10.1080/10106049.2022.2066202
52. Landslide susceptibility modeling assisted by Persistent Scatterers Interferometry (PSI): an example from the northwestern coast of Malta D. Piacentini et al. https://doi.org/10.1007/s11069-015-1740-8
53. Landslide Hazard and Exposure Modelling in Data‐Poor Regions: The Example of the Rohingya Refugee Camps in Bangladesh R. Emberson et al. https://doi.org/10.1029/2020EF001666
54. Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines H. Hong et al. https://doi.org/10.1016/j.catena.2015.05.019
55. Landslide-risk mapping in a developing hilly area with limited information on landslide occurrence J. Jiménez-Perálvarez https://doi.org/10.1007/s10346-017-0903-y
56. Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories R. Emberson et al. https://doi.org/10.5194/nhess-22-1129-2022
57. Spatiotemporal landslide susceptibility mapping using machine learning models: A case study from district Hattian Bala, NW Himalaya, Pakistan A. Hammad Khaliq et al. https://doi.org/10.1016/j.asej.2022.101907
58. Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review N. Saleem et al. https://doi.org/10.3390/ijgi8120545
59. Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues F. Catani et al. https://doi.org/10.5194/nhess-13-2815-2013
60. The Open Landslide Project (OLP), a New Inventory of Shallow Landslides for Susceptibility Models: The Autumn 2019 Extreme Rainfall Event in the Langhe-Monferrato Region (Northwestern Italy) M. Licata et al. https://doi.org/10.3390/geosciences13100289
61. The performance of landslide susceptibility models critically depends on the quality of digital elevation models J. Brock et al. https://doi.org/10.1080/19475705.2020.1776403
62. Flash-Flood Potential assessment in the upper and middle sector of Prahova river catchment (Romania). A comparative approach between four hybrid models R. Costache https://doi.org/10.1016/j.scitotenv.2018.12.397
63. Landslide–Anthropogenic Interactions in Urban Areas: A Multidisciplinary Case Study from Taounate, Morocco R. Sahrane et al. https://doi.org/10.1007/s10706-025-03204-0
64. Landslide susceptibility mapping using hybridized block modular intelligence model A. Abbaszadeh Shahri & F. Maghsoudi Moud https://doi.org/10.1007/s10064-020-01922-8
65. Digital Elevation Models of Rockfalls and Landslides: A Review and Meta-Analysis M. Kakavas & K. Nikolakopoulos https://doi.org/10.3390/geosciences11060256
66. Impact of orthogonal transformation for factors on model performance in landslide susceptibility Q. Liu et al. https://doi.org/10.1007/s12665-023-10803-8
67. Dynamic path-dependent landslide susceptibility modelling J. Samia et al. https://doi.org/10.5194/nhess-20-271-2020
68. Landslide Detection Using Densely Connected Convolutional Networks and Environmental Conditions H. Cai et al. https://doi.org/10.1109/JSTARS.2021.3079196
69. Using machine learning techniques in multi-hazards assessment of Golestan National Park, Iran H. Faramarzi et al. https://doi.org/10.1007/s11069-023-05984-1
70. Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping E. Sahin https://doi.org/10.1080/10106049.2020.1831623
71. Assessing landslide susceptibility using frequency ratio and logistic regression models in the Khudi Watershed S. Hamal et al. https://doi.org/10.1186/s40562-026-00467-0
72. The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China K. Zhang et al. https://doi.org/10.1007/s12665-017-6731-5
73. Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: A case of the Belice River basin (western Sicily, Italy) C. Conoscenti et al. https://doi.org/10.1016/j.geomorph.2014.09.020
74. A Novel Relevance Vector Machine Classifier with Cuckoo Search Optimization for Spatial Prediction of Landslides N. Hoang & D. Tien Bui https://doi.org/10.1061/(ASCE)CP.1943-5487.0000557
75. Mapping Susceptibility to Debris Flows Triggered by Tropical Storms: A Case Study of the San Vicente Volcano Area (El Salvador, CA) C. Mercurio et al. https://doi.org/10.3390/earth2010005
76. Feature-based model for landslide susceptibility mapping using a multi-parametric decision-making technique and the analytic hierarchy process L. Bopche & P. Rege https://doi.org/10.1007/s12046-021-01648-7
77. 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
78. Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China P. Tsangaratos et al. https://doi.org/10.1007/s10346-016-0769-4
79. Mapping at-risk transportation infrastructure assets using statistical and machine learning methods R. Salunke & S. Khan https://doi.org/10.1038/s41598-025-18440-w
80. Introducing a novel multi-layer perceptron network based on stochastic gradient descent optimized by a meta-heuristic algorithm for landslide susceptibility mapping H. Hong et al. https://doi.org/10.1016/j.scitotenv.2020.140549
81. An Event-Based Inventory Approach in Landslide Hazard Assessment: The Case of the Skolis Mountain, Northwest Peloponnese, Greece A. Litoseliti et al. https://doi.org/10.3390/ijgi9070457
82. Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy) D. Costanzo et al. https://doi.org/10.1007/s10346-013-0415-3
83. Automated derivation and spatio-temporal analysis of landslide properties in southern Kyrgyzstan D. Golovko et al. https://doi.org/10.1007/s11069-016-2636-y
84. Landslide susceptibility mapping using GIS Matrix Method and Frequency Ratio, application in the marly context of Moulay Yacoub Region, Morocco I. Obda et al. https://doi.org/10.1051/bsgf/2023016
85. An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district T. Kavzoglu et al. https://doi.org/10.1007/s11069-014-1506-8
86. The GEMMA (Geo-EnvironMental multivariate analysis) toolbox: A user-friendly software for multivariate analysis F. Pilade et al. https://doi.org/10.1016/j.cageo.2025.105914
87. Evaluation of Landslide Risk Using the WoE and IV Methods: A Case Study in the Zipaquirá–Pacho Road Corridor S. Velazco et al. https://doi.org/10.3390/geohazards6020027
88. GIS-based landslide spatial modeling in Ganzhou City, China H. Hong et al. https://doi.org/10.1007/s12517-015-2094-y
89. Application of geographically weighted principal component analysis and fuzzy approach for unsupervised landslide susceptibility mapping on Gish River Basin, India T. Basu et al. https://doi.org/10.1080/10106049.2020.1778105
90. Debris flow susceptibility mapping using frequency ratio and seed cells, in a portion of a mountain international route, Dry Central Andes of Argentina M. Esper Angillieri https://doi.org/10.1016/j.catena.2020.104504
91. A comparative study of support vector machine and logistic model tree classifiers for shallow landslide susceptibility modeling M. Abedini et al. https://doi.org/10.1007/s12665-019-8562-z
92. Discussion on the tree-based machine learning model in the study of landslide susceptibility Q. Liu et al. https://doi.org/10.1007/s11069-022-05329-4
93. Regional landslide susceptibility zonation utilizing bivariate statistical techniques in the northwestern Himalayas, Jammu and Kashmir, India I. Khan et al. https://doi.org/10.1007/s12040-024-02367-3
94. An Ensemble Broad Learning System (BLS) for Evaluating Landslide Susceptibility in Taiyuan City, Northern China D. Zhao et al. https://doi.org/10.3390/app13148409
95. Comparative Analysis of Machine Learning, Statistical, and MCDA Methods for Rainfall-Induced Landslide Susceptibility Mapping in the Eco-Sensitive Koyna River Basin of India A. Patil et al. https://doi.org/10.1007/s40098-024-00957-y
96. Landslide susceptibility mapping using hybrid machine learning classifiers: a case study of Neelum Valley, Pakistan S. Meena et al. https://doi.org/10.1007/s10064-025-04270-7
97. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree D. Tien Bui et al. https://doi.org/10.1007/s10346-015-0557-6
98. Landslide susceptibility mapping at sin Ho, Lai Chau province, Vietnam using ensemble models based on fuzzy unordered rules induction algorithm T. Bien et al. https://doi.org/10.1080/10106049.2022.2136253
99. GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran) H. Pourghasemi et al. https://doi.org/10.1007/s12517-012-0825-x
100. Improving landslide susceptibility mapping using morphometric features in the Mawat area, Kurdistan Region, NE Iraq: Comparison of different statistical models A. Othman et al. https://doi.org/10.1016/j.geomorph.2018.07.018
101. Integrated geospatial, rainfall threshold and physical based modelling for landslides in parts of upper bhagirathi basin, Uttarakhand P. Mohan et al. https://doi.org/10.1016/j.qsa.2024.100172
102. A modified frequency ratio method for landslide susceptibility assessment L. Li et al. https://doi.org/10.1007/s10346-016-0771-x
103. Coupled model for simulation of landslides and debris flows at local scale D. Park et al. https://doi.org/10.1007/s11069-016-2150-2
104. Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector Classification D. Xia et al. https://doi.org/10.3390/rs14112707
105. Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods E. Bravo-López et al. https://doi.org/10.3390/land12061135
106. Comparing Forward Conditional Analysis and Forward Logistic Regression Methods in a Landslide Susceptibility Assessment: A Case Study in Sicily D. Costanzo & C. Irigaray https://doi.org/10.3390/hydrology7030037
107. Comparative landslide susceptibility assessment using information value and frequency ratio bivariate statistical methods: a case study from Northwestern Himalayas, Jammu and Kashmir, India I. Khan et al. https://doi.org/10.1007/s12517-024-12022-2
108. MAST: An Earthquake-Triggered Landslides Extraction Method Combining Morphological Analysis Edge Recognition With Swin-Transformer Deep Learning Model Y. Huang et al. https://doi.org/10.1109/JSTARS.2023.3342989
109. Using RS and GIS for risk management of natural disasters consequences: The case of cultural heritage in Jinan city, China G. Wei et al. https://doi.org/10.1016/j.heliyon.2024.e38217
110. Brief communication: Visualizing uncertainties in landslide susceptibility modelling using bivariate mapping M. Schlögl et al. https://doi.org/10.5194/nhess-25-1425-2025
111. Advanced landslide susceptibility mapping and analysis of driving mechanisms using ensemble machine learning models M. Maashi et al. https://doi.org/10.1016/j.jsames.2024.105272
112. 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
113. Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment D. Bui et al. https://doi.org/10.1016/j.catena.2019.104426
114. Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest E. Sahin https://doi.org/10.1007/s42452-020-3060-1
115. Landslide susceptibility mapping using deep learning models in Ardabil province, Iran H. Hamedi et al. https://doi.org/10.1007/s00477-022-02263-6
116. Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree X. Truong et al. https://doi.org/10.3390/app8071046
117. An integrative approach of the physical-based stability index mapping with the maximum entropy stochastic model for risk analysis of mass movements H. Memarian et al. https://doi.org/10.1007/s10668-022-02165-1
118. Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models H. Hong et al. https://doi.org/10.1016/j.geomorph.2016.02.012
119. Landslide susceptibility classification using multi hive artificial bee colony programming: A novel symbolic regression framework S. Arslan et al. https://doi.org/10.1016/j.eswa.2025.129324
120. A Combined Method for Preparation of Landslide Susceptibility Map in Izmir (Türkiye) C. KINCAL & H. KAYHAN https://doi.org/10.3390/app12189029
121. 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/10.2139/ssrn.4144329
122. Predicting the landslides triggered by the 2009 96E/Ida tropical storms in the Ilopango caldera area (El Salvador, CA): optimizing MARS-based model building and validation strategies E. Rotigliano et al. https://doi.org/10.1007/s12665-019-8214-3
123. VAN İLİ HEYELAN DUYARLILIĞININ FREKANS ORANI YÖNTEMİYLE ANALİZİ N. ÜZEL & D. ÖZTÜRK https://doi.org/10.17482/uumfd.969246
124. Using the rotation and random forest models of ensemble learning to predict landslide susceptibility L. Zhao et al. https://doi.org/10.1080/19475705.2020.1803421
125. Prediction of the landslide susceptibility: Which algorithm, which precision? H. Pourghasemi & O. Rahmati https://doi.org/10.1016/j.catena.2017.11.022
126. Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization D. Tien Bui et al. https://doi.org/10.1007/s10346-016-0711-9
127. Slope-Unit Scale Landslide Susceptibility Mapping Based on the Random Forest Model in Deep Valley Areas H. Deng et al. https://doi.org/10.3390/rs14174245
128. Rockfall Mapping and Monitoring Across the Kalymnos Sport Rock Climbing Sites, Based on Ultra-High-Resolution Remote Sensing Data and Integrated Simulations E. Vassilakis et al. https://doi.org/10.3390/land13111873
129. Optimization method of conditioning factors selection and combination for landslide susceptibility prediction F. Huang et al. https://doi.org/10.1016/j.jrmge.2024.04.029
130. Estimating landslide hazard distribution based on machine learning and bivariate statistics in Utmah Region, Yemen Y. Khalil et al. https://doi.org/10.1007/s11069-023-06310-5
131. A comprehensive analysis of landslide susceptibility in Iyidere Basin (NE, Turkey) using machine learning techniques and statistical bivariate methods K. Ersayin & A. Uzun https://doi.org/10.1007/s11069-025-07354-5
132. Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment (Sicily, Italy) M. Cama et al. https://doi.org/10.1007/s12665-015-5047-6
133. Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area Q. Su et al. https://doi.org/10.3390/ijgi6070228
134. Identification of the significant parameters in spatial prediction of landslide hazard A. Tyagi et al. https://doi.org/10.1007/s10064-023-03334-w
135. Review on remote sensing methods for landslide detection using machine and deep learning A. Mohan et al. https://doi.org/10.1002/ett.3998
136. Modelling terrain erosion susceptibility of logged and regenerated forested region in northern Borneo through the Analytical Hierarchy Process (AHP) and GIS techniques H. Vijith & D. Dodge-Wan https://doi.org/10.1186/s40677-019-0124-x
137. Application of environmental variables in statistically-based landslide susceptibility mapping: A review X. Zhao et al. https://doi.org/10.3389/feart.2023.1147427
138. Landslide susceptibility: a statistically-based assessment on a depositional pyroclastic ramp F. Murillo-García et al. https://doi.org/10.1007/s11629-018-5225-6
139. Improving transferability strategies for debris flow susceptibility assessment: Application to the Saponara and Itala catchments (Messina, Italy) M. Cama et al. https://doi.org/10.1016/j.geomorph.2017.03.025
140. Development of a region-partitioning method for debris flow susceptibility mapping S. Qiao et al. https://doi.org/10.1007/s11629-020-6497-1
141. Landslide risk assessment considering socionatural factors: methodology and application to Cubatão municipality, São Paulo, Brazil P. Hader et al. https://doi.org/10.1007/s11069-021-04991-4
142. Landslide Susceptibility Modeling Using the Index of Entropy and Frequency Ratio Method from Nefas-Mewcha to Weldiya Road Corridor, Northwestern Ethiopia A. Wubalem et al. https://doi.org/10.1007/s10706-022-02214-6
143. Landslide susceptibility and risk analysis in Benighat Rural Municipality, Dhading, Nepal G. Dhungana et al. https://doi.org/10.1016/j.nhres.2023.03.006
144. Exploring aspects affecting the predicted capacity of landslide susceptibility based on machine learning technology Q. Liu & A. Tang https://doi.org/10.1080/10106049.2022.2088863
145. Expert judgement criteria for mapping landslide susceptibility in Tangse Sub-District, Pidie District, Aceh Province, Indonesia H. Basri et al. https://doi.org/10.1088/1755-1315/667/1/012059
146. Modeling Shallow Landslide Susceptibility and Assessment of the Relative Importance of Predisposing Factors, through a GIS-Based Statistical Analysis M. Conforti & F. Ietto https://doi.org/10.3390/geosciences11080333
147. Landslide Susceptibility Mapping of Landslides with Artificial Neural Networks: Multi-Approach Analysis of Backpropagation Algorithm Applying the Neuralnet Package in Cuenca, Ecuador E. Bravo-López et al. https://doi.org/10.3390/rs14143495
148. Landslide Susceptibility Analysis on the Vicinity of Bogotá-Villavicencio Road (Eastern Cordillera of the Colombian Andes) M. Herrera-Coy et al. https://doi.org/10.3390/rs15153870
149. A Simplified ArcGIS Approach for Landslides Risk Assessment in the Province of Bergamo B. Marana https://doi.org/10.4236/jgis.2017.96044
150. Heavy Rainfall and Landslide Event in January 1831 at the Pedregoso Mountains (Cabeza Del Buey, SW Spain) J. García-Garrido et al. https://doi.org/10.3390/atmos11050544
151. GIS-based landslide and rockfall susceptibility zoning in Chepelarska River Basin (Western Rhodope Mountains, Bulgaria) E. Tcherkezova et al. https://doi.org/10.52321/igh.37.1.117
152. Investigating the effects of landscape characteristics on landslide susceptibility and frequency-area distributions: the case of Taounate province, Northern Morocco R. Sahrane et al. https://doi.org/10.1080/10106049.2022.2134462
153. Development of integrated deep learning and machine learning algorithm for the assessment of landslide hazard potential B. Aslam et al. https://doi.org/10.1007/s00500-021-06105-5
154. Shallow landslides susceptibility assessment in different environments M. Persichillo et al. https://doi.org/10.1080/19475705.2016.1265011
155. Combining multi-typologies landslide susceptibility maps: a case study for the Visso area (central Italy) C. Martinello et al. https://doi.org/10.1080/17445647.2023.2198148
156. Landslide susceptibility mapping (LSM) of Swat District, Hindu Kush Himalayan region of Pakistan, using GIS-based bivariate modeling F. Islam et al. https://doi.org/10.3389/fenvs.2022.1027423
157. GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks D. Tien Bui et al. https://doi.org/10.1007/s12665-016-5919-4
158. Investigating Limits in Exploiting Assembled Landslide Inventories for Calibrating Regional Susceptibility Models: A Test in Volcanic Areas of El Salvador C. Martinello et al. https://doi.org/10.3390/app12126151
159. Projected Rainfall Triggered Landslide Susceptibility Changes in the Hengduan Mountain Region, Southwest China under 1.5–4.0 °C Warming Scenarios Based on CMIP6 Models H. Yin et al. https://doi.org/10.3390/atmos14020214
160. A holistic approach of remote sensing, GIS, and machine learning for shallow landslide susceptibility mapping in Gaganbawada region of Western Ghats, India A. Patil & S. Panhalkar https://doi.org/10.1007/s43538-024-00305-x