103 citations as recorded by crossref.

1. Landslide Susceptibility Assessment of Heraklion Prefecture in Crete, Greece S. Adam et al. https://doi.org/10.12681/bgsg.28703
2. 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
3. Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models R. Schlögel et al. https://doi.org/10.1016/j.geomorph.2017.10.018
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5. A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey) G. Hasekioğulları & M. Ercanoglu https://doi.org/10.1007/s11069-012-0218-1
6. A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods G. Demir et al. https://doi.org/10.1007/s11069-012-0418-8
7. Development of a Coupled Spatiotemporal Algal Bloom Model for Coastal Areas: A Remote Sensing and Data Mining-Based Approach R. Elkadiri et al. https://doi.org/10.1109/JSTARS.2016.2555898
8. Assessment of landslide-triggering factors and occurrence using morphometric parameters in Geyraz Basin, Tokat, Northern Turkey T. Bayer Altın & E. Gökkaya https://doi.org/10.1007/s12665-018-7315-8
9. Landslide susceptibility analysis for the Kerch Peninsula using weights of evidence approach and GIS D. Krivoguz & L. Bespalova https://doi.org/10.2205/2019ES000682
10. 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
11. Assessment of shallow landslide susceptibility using an artificial neural network B. Zeng & X. Chen https://doi.org/10.1007/s12517-021-06843-8
12. Landslide susceptibility analysis using Probabilistic Certainty Factor Approach: A case study on Tevankarai stream watershed, India E. Sujatha et al. https://doi.org/10.1007/s12040-012-0230-6
13. Artificial intelligence for remote sensing-based detection and prediction of landslides in Malaysia and Vietnam: A state-of-the-art review W. Loke et al. https://doi.org/10.1007/s11069-026-08316-1
14. Cave entrance location model using binary logistic regression: the case study of south Gombong karst region, Indonesia R. Putra et al. https://doi.org/10.2298/IJGI2203229P
15. Lineament mapping and its application in landslide hazard assessment: a review M. Ramli et al. https://doi.org/10.1007/s10064-009-0255-5
16. Spatial analysis of salinity distribution patterns in upper layers of the Black Sea D. Krivoguz et al. https://doi.org/10.1051/e3sconf/202020303010
17. Study on Dynamic Stability Prediction Model of Slope in Eastern Tibet Section of Sichuan-Tibet Highway T. Ye et al. https://doi.org/10.1155/2022/4230674
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20. Integrated approach for determining spatio-temporal variations in the hydrodynamic factors as a contributing parameter in landslide susceptibility assessments M. Canoglu et al. https://doi.org/10.1007/s10064-018-1337-z
21. Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016) H. Pourghasemi et al. https://doi.org/10.1007/s12517-018-3531-5
22. Assembly area risk assessment methodology for post-flood evacuation by integrated neutrosophic AHP-CODAS I. Sahmutoglu et al. https://doi.org/10.1007/s11069-022-05712-1
23. Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy) M. Conforti et al. https://doi.org/10.1016/j.catena.2013.08.006
24. Representative Elementary Volume Estimation and Neural Network-Based Prediction of Change Rates of Dense Non-Aqueous Phase Liquid Saturation and Dense Non-Aqueous Phase Liquid–Water Interfacial Area in Porous Media Z. Cheng et al. https://doi.org/10.3390/separations10080446
25. 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
26. 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
27. Landslide Susceptibility Mapping Using Weighted-Overlay Approach in Rangamati, Bangladesh M. Khatun et al. https://doi.org/10.1007/s41748-022-00312-2
28. Landslide Hazard and Risk Mapping Using the Weighted Linear Combination Model Applied to the Tevankarai Stream Watershed, Kodaikkanal, India E. Sujatha & G. Rajamanickam https://doi.org/10.1080/10807039.2014.920222
29. Linear Parameters Causing Landslides: A Case Study of Distance to the Road, Fault, Drainage S. ÇELLEK https://doi.org/10.34088/kojose.1117817
30. A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides B. Pradhan et al. https://doi.org/10.3390/rs13224521
31. Landslide susceptibility mapping using Genetic Algorithm for the Rule Set Production (GARP) model F. Adineh et al. https://doi.org/10.1007/s11629-018-4833-5
32. Quaternary Rock Uplift Rates and Their Implications for the Western Flank of the North Anatolian Fault Restraining Bend; Inferences From Fluvial Terrace Ages K. McClain et al. https://doi.org/10.1029/2019TC005993
33. 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
34. Application of machine learning algorithms in landslide susceptibility mapping, Kali Valley, Kumaun Himalaya, India A. Solanki et al. https://doi.org/10.1080/10106049.2022.2120546
35. Shallow Landslide Susceptibility Models Based on Artificial Neural Networks Considering the Factor Selection Method and Various Non-Linear Activation Functions D. Lee et al. https://doi.org/10.3390/rs12071194
36. Landslide susceptibility mapping at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms A. Can et al. https://doi.org/10.1007/s10064-017-1034-3
37. Utilization of Open Source Spatial Data for Landslide Susceptibility Mapping at Chittagong District of Bangladesh—An Appraisal for Disaster Risk Reduction and Mitigation Approach M. Islam et al. https://doi.org/10.4236/ijg.2017.84031
38. Landslide hazard assessment: recent trends and techniques S. Pardeshi et al. https://doi.org/10.1186/2193-1801-2-523
39. Optimizing slope unit-based landslide susceptibility mapping using the priority-flood flow direction algorithm G. Yan et al. https://doi.org/10.1016/j.catena.2023.107657
40. Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms P. Sajadi et al. https://doi.org/10.1186/s40562-022-00218-x
41. An integrated approach of hybrid ensemble machine learning-based efficient seismic slope fragility assessment and GIS mapping R. Mostafizur et al. https://doi.org/10.1038/s41598-026-38171-w
42. Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district, Bartın, northern Turkey A. Eker et al. https://doi.org/10.1080/13658816.2014.953164
43. Survey of spatial and temporal landslide prediction methods and techniques https://doi.org/10.7744/kjoas.20160053
44. Landslide susceptibility analysis of Karakoram highway using analytical hierarchy process and scoops 3D B. Rashid et al. https://doi.org/10.1007/s11629-018-5195-8
45. Comparison of slope stabilization methods by three-dimensional finite element analysis O. Usluogullari et al. https://doi.org/10.1007/s11069-015-2118-7
46. Application of Chebyshev theorem to data preparation in landslide susceptibility mapping studies: an example from Yenice (Karabük, Turkey) region M. Ercanoglu et al. https://doi.org/10.1007/s11629-016-3880-z
47. A comparison of GIS-based landslide susceptibility assessment of the Satuk village (Yenice, NW Turkey) by frequency ratio and multi-criteria decision methods D. Arca et al. https://doi.org/10.1007/s12665-019-8094-6
48. GIS-based multicriteria evaluation with multiscale analysis to characterize urban landslide susceptibility in data-scarce environments S. Dragićević et al. https://doi.org/10.1016/j.habitatint.2014.06.031
49. Investigation of slope movements and landslide susceptibility analysis of Karabük-Yenice area in NW of Turkey A. Yiğittepe et al. https://doi.org/10.1007/s12517-021-06838-5
50. A Novel Classifier Based on Composite Hyper-cubes on Iterated Random Projections for Assessment of Landslide Susceptibility B. Pham https://doi.org/10.1007/s12594-018-0862-5
51. GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey) C. Yilmaz et al. https://doi.org/10.1007/s12665-011-1196-4
52. A geotechnical approach to compare different slope stabilization techniques for failed slope in the Darjeeling hills, India A. Chawla et al. https://doi.org/10.1007/s12665-023-11054-3
53. Landslide susceptibility mapping through continuous fuzzification and geometric average multi-criteria decision-making approaches V. Ghiasi et al. https://doi.org/10.1007/s11069-021-04606-y
54. Application of frequency ratio (FR) model in spatial prediction of landslides in the Balason river basin, Darjeeling Himalaya S. Mondal & S. Mandal https://doi.org/10.1007/s41324-017-0101-y
55. 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
56. Statistical Analysis of the Potential of Landslides Induced by Combination between Rainfall and Earthquakes C. Tseng et al. https://doi.org/10.3390/w14223691
57. Is there a consistency in basin morphometry and hydrodynamic modelling results in terms of the flood generation potential of basins? A case study from the Ulus River Basin (Türkiye) H. Ozdemir & A. Akbas https://doi.org/10.1016/j.jhydrol.2023.129926
58. Comparing data-driven landslide susceptibility models based on participatory landslide inventory mapping in Purwosari area, Yogyakarta, Java G. Samodra et al. https://doi.org/10.1007/s12665-017-6475-2
59. Landslide susceptibility modeling based on remote sensing data and data mining techniques X. Wang et al. https://doi.org/10.1007/s12665-022-10195-1
60. Mapping landslide susceptibility in the Debretabor-Alember road sector, Northwestern Ethiopia through geospatial tools and statistical approaches B. Tesfaye et al. https://doi.org/10.15243/jdmlm.2024.112.5169
61. GIS-based landslide susceptibility assessment: a case study in Kelemen Valley (Yenice—Karabuk, NW Turkey) I. Tasoglu et al. https://doi.org/10.1007/s12665-016-6098-z
62. Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China) Y. Wang et al. https://doi.org/10.3390/ijerph17124206
63. Landslide identification using machine learning techniques: Review, motivation, and future prospects S. S. et al. https://doi.org/10.1007/s12145-022-00889-2
64. Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping E. Sahin https://doi.org/10.1080/10106049.2020.1831623
65. Landslide susceptibility analysis using probabilistic likelihood ratio model—a geospatial-based study E. Sujatha et al. https://doi.org/10.1007/s12517-011-0356-x
66. 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
67. Optimal landslide susceptibility zonation based on multiple forecasts M. Rossi et al. https://doi.org/10.1016/j.geomorph.2009.06.020
68. 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
69. A review on spatial, temporal and magnitude prediction of landslide hazard A. Tyagi et al. https://doi.org/10.1016/j.jaesx.2022.100099
70. Landslide susceptibility zonation mapping using GIS-based frequency ratio model with multi-class spatial data-sets in the Adwa-Adigrat mountain chains, northern Ethiopia G. Berhane et al. https://doi.org/10.1016/j.jafrearsci.2020.103795
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73. Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment M. Sameen et al. https://doi.org/10.1016/j.catena.2019.104249
74. Landslide susceptibility analysis for the Kerch Peninsula using weights of evidence approach and GIS D. Krivoguz & L. Bespalova https://doi.org/10.2205/2020ES000682
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79. Utilization of streamflow rates for determination of precipitation thresholds for landslides in a data-scarce region (Eastern Bartın, NW Türkiye) P. Avcı & M. Ercanoglu https://doi.org/10.1007/s12665-024-11487-4
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83. Landslide susceptibility map of Bogor Area using analytical hierarchy process Y. Wicaksono et al. https://doi.org/10.1088/1755-1315/538/1/012050
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