102 citations as recorded by crossref.

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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. 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
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22. 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
23. 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
24. 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
25. 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
26. Landslide Susceptibility Mapping Using Weighted-Overlay Approach in Rangamati, Bangladesh M. Khatun et al. https://doi.org/10.1007/s41748-022-00312-2
27. 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
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30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
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38. 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
39. 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
40. 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
41. 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
42. Survey of spatial and temporal landslide prediction methods and techniques https://doi.org/10.7744/kjoas.20160053
43. 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
44. Comparison of slope stabilization methods by three-dimensional finite element analysis O. Usluogullari et al. https://doi.org/10.1007/s11069-015-2118-7
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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
55. Statistical Analysis of the Potential of Landslides Induced by Combination between Rainfall and Earthquakes C. Tseng et al. https://doi.org/10.3390/w14223691
56. 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
57. 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
58. 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
59. 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
60. 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
61. 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
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63. Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping E. Sahin https://doi.org/10.1080/10106049.2020.1831623
64. 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
65. 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
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67. 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
68. A review on spatial, temporal and magnitude prediction of landslide hazard A. Tyagi et al. https://doi.org/10.1016/j.jaesx.2022.100099
69. 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. Landslide susceptibility analysis for the Kerch Peninsula using weights of evidence approach and GIS D. Krivoguz & L. Bespalova https://doi.org/10.2205/2020ES000682
74. Landslide susceptibility mapping using integrated approach of multi-criteria and geospatial techniques at Nilgiris district of India B. Biswas et al. https://doi.org/10.1007/s12517-021-07341-7
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78. 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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