50 citations as recorded by crossref.

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5. A Remote Sensing-Based Approach for Debris-Flow Susceptibility Assessment Using Artificial Neural Networks and Logistic Regression Modeling R. Elkadiri et al. 10.1109/JSTARS.2014.2337273
6. Landslide susceptibility mapping at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms A. Can et al. 10.1007/s10064-017-1034-3
7. Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods B. Pham et al. 10.1007/s00704-015-1702-9
8. Spatial Proximity-Based Geographically Weighted Regression Model for Landslide Susceptibility Assessment: A Case Study of Qingchuan Area, China Y. Li et al. 10.3390/app10031107
9. A Comparison of an Adaptive Neuro-Fuzzy and Frequency Ratio Model to Landslide-Susceptibility Mapping along Forest Road Networks N. Zare et al. 10.3390/f12081087
10. Performance analysis of landslide susceptibility assessment under different factor-filtering models Y. Li et al. 10.1007/s12517-021-07532-2
11. Spatial predictive modelling of rainfall- and earthquake-induced landslide susceptibility in the Himalaya region of Uttarakhand, India . Sangeeta & B. Maheshwari 10.1007/s12665-022-10352-6
12. Dynamic Landslide Susceptibility Analysis that Combines Rainfall Period, Rainfall, and Geospatial Information J. Lee et al. 10.2139/ssrn.4093941
13. Landslide Mapping Using Multiscale LiDAR Digital Elevation Models J. Miandad et al. 10.2113/EEG-2268
14. Spatial prediction of landslide susceptibility using a decision tree approach: a case study of the Pyeongchang area, Korea I. Park & S. Lee 10.1080/01431161.2014.943326
15. An interpretable model for bridge scour risk assessment using explainable artificial intelligence and engineers’ expertise T. Wang et al. 10.1080/15732479.2023.2230564
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18. An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan J. Dou et al. 10.1007/s11069-015-1799-2
19. A discussion on the development model of earthquake relic geopark – a case study of the Qingchuan Earthquake Relic Geopark in Sichuan Province, China L. Wang & M. Tian 10.1016/j.culher.2013.11.007
20. The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW China Z. Chen et al. 10.1007/s11069-020-03899-9
21. Sustainable GIS Based-ANN's Solution for Landfill Suitability Analysis S. Abujayyab et al. 10.4028/www.scientific.net/AMM.802.537
22. Failure probability assessment of landslides triggered by earthquakes and rainfall: a case study in Yadong County, Tibet, China L. Chen et al. 10.1038/s41598-020-73727-4
23. Evaluation of landslide susceptibility models: A comparative study on the part of Western Ghat Region, India S. Bera et al. 10.1016/j.rsase.2018.10.010
24. Overview: Documentation and monitoring of landslides and debris flows for mathematical modelling and design of mitigation measures – outcomes of the EGU 2011, NH session L. Franzi et al. 10.5194/nhess-13-2013-2013
25. GIS-Based Integration of Subjective and Objective Weighting Methods for Regional Landslides Susceptibility Mapping S. Zhou et al. 10.3390/su8040334
26. Coupling logistic model tree and random subspace to predict the landslide susceptibility areas with considering the uncertainty of environmental features X. Luo et al. 10.1038/s41598-019-51941-z
27. Surrogate Model-Based Prediction of Settlement in Foundation Over Cavity for Reliability Analysis K. Shubham et al. 10.1007/s40515-023-00329-8
28. Mapping the susceptibility of rainfall and earthquake triggered landslides along China–Nepal highways K. Gnyawali et al. 10.1007/s10064-019-01583-2
29. Estimating landslide susceptibility areas considering the uncertainty inherent in modeling methods H. Kim et al. 10.1007/s00477-018-1609-y
30. A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications H. Moayedi et al. 10.1007/s00521-019-04109-9
31. Riverside Landslide Susceptibility Overview: Leveraging Artificial Neural Networks and Machine Learning in Accordance with the United Nations (UN) Sustainable Development Goals Y. Nanehkaran et al. 10.3390/w15152707
32. An integrated approach for landslide susceptibility mapping by considering spatial correlation and fractal distribution of clustered landslide data L. Liu et al. 10.1007/s10346-018-01122-2
33. Landslide susceptibility zonation using analytical hierarchy process and GIS for the Bojnurd region, northeast of Iran M. Mansouri Daneshvar 10.1007/s10346-013-0458-5
34. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea L. Saro et al. 10.1515/geo-2016-0010
35. Forecasting and validation of landslide susceptibility using an integration of frequency ratio and neuro-fuzzy models: a case study of Seorak mountain area in Korea M. Lee et al. 10.1007/s12665-015-4048-9
36. Exploring spatial non-stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression C. Chalkias et al. 10.1007/s10064-020-01733-x
37. Application of deep learning algorithms in geotechnical engineering: a short critical review W. Zhang et al. 10.1007/s10462-021-09967-1
38. A systematic review of landslide probability mapping using logistic regression M. Budimir et al. 10.1007/s10346-014-0550-5
39. Application of an ensemble learning model based on random subspace and a J48 decision tree for landslide susceptibility mapping: a case study for Qingchuan, Sichuan, China Y. Li et al. 10.1007/s12665-022-10389-7
40. Machine Learning Models for Predicting Shear Wave Velocity of Soils Z. Mousavi et al. 10.1088/1755-1315/1334/1/012039
41. Scale and spatial distribution assessment of rainfall-induced landslides in a catchment with mountain roads C. Tseng et al. 10.5194/nhess-18-687-2018
42. Spatial Non-Stationarity-Based Landslide Susceptibility Assessment Using PCAMGWR Model Y. Li et al. 10.3390/w14060881
43. Research on landslide hazard assessment in data-deficient areas: a case study of Tumen City, China X. Li et al. 10.1007/s11600-023-01057-w
44. Application of Chebyshev theorem to data preparation in landslide susceptibility mapping studies: an example from Yenice (Karabük, Turkey) region M. Ercanoglu et al. 10.1007/s11629-016-3880-z
45. Artificial neural network for landslide vulnerability mapping in Leitimur Peninsula Ambon Island F. Puturuhu et al. 10.1088/1755-1315/989/1/012013
46. A review of recent earthquake-induced landslides on the Tibetan Plateau B. Zhao et al. 10.1016/j.earscirev.2023.104534
47. Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms M. Shafapourtehrany et al. 10.3390/rs15184501
48. Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models C. Polykretis & C. Chalkias 10.1007/s11069-018-3299-7
49. Landslide Susceptibility Assessment at the Basin Scale for Rainfall- and Earthquake-Triggered Shallow Slides C. Gordo et al. 10.3390/geosciences9060268
50. Perspective on secondary disasters: a literature review for future research K. Sahın et al. 10.1007/s10668-024-05577-3