58 citations as recorded by crossref.

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19. An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan J. Dou et al. https://doi.org/10.1007/s11069-015-1799-2
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21. The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW China Z. Chen et al. https://doi.org/10.1007/s11069-020-03899-9
22. Sustainable GIS Based-ANN's Solution for Landfill Suitability Analysis S. Abujayyab et al. https://doi.org/10.4028/www.scientific.net/AMM.802.537
23. Exploring the spatial distribution patterns of loess landslides and identifying the key driving factors in typical regions of the upper Yellow River, China H. Shu et al. https://doi.org/10.1080/19475705.2026.2636709
24. Failure probability assessment of landslides triggered by earthquakes and rainfall: a case study in Yadong County, Tibet, China L. Chen et al. https://doi.org/10.1038/s41598-020-73727-4
25. Evaluation of landslide susceptibility models: A comparative study on the part of Western Ghat Region, India S. Bera et al. https://doi.org/10.1016/j.rsase.2018.10.010
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27. GIS-Based Integration of Subjective and Objective Weighting Methods for Regional Landslides Susceptibility Mapping S. Zhou et al. https://doi.org/10.3390/su8040334
28. Coupling logistic model tree and random subspace to predict the landslide susceptibility areas with considering the uncertainty of environmental features X. Luo et al. https://doi.org/10.1038/s41598-019-51941-z
29. Surrogate Model-Based Prediction of Settlement in Foundation Over Cavity for Reliability Analysis K. Shubham et al. https://doi.org/10.1007/s40515-023-00329-8
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31. Mapping the susceptibility of rainfall and earthquake triggered landslides along China–Nepal highways K. Gnyawali et al. https://doi.org/10.1007/s10064-019-01583-2
32. Estimating landslide susceptibility areas considering the uncertainty inherent in modeling methods H. Kim et al. https://doi.org/10.1007/s00477-018-1609-y
33. Spatio-temporal variability of landslides as indicator of climate change impact: Towards an Italian national scale integrated procedure for the rainfall role analysis in landslide dynamics M. Delchiaro et al. https://doi.org/10.1016/j.ijdrr.2026.106252
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36. An integrated approach for landslide susceptibility mapping by considering spatial correlation and fractal distribution of clustered landslide data L. Liu et al. https://doi.org/10.1007/s10346-018-01122-2
37. Landslide susceptibility zonation using analytical hierarchy process and GIS for the Bojnurd region, northeast of Iran M. Mansouri Daneshvar https://doi.org/10.1007/s10346-013-0458-5
38. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea L. Saro et al. https://doi.org/10.1515/geo-2016-0010
39. Road network resilience assessment based on full-scenario disaster simulation: landslide movement, emergency, and stabilization X. Chen et al. https://doi.org/10.1016/j.ress.2026.112572
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41. 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. https://doi.org/10.1007/s10064-020-01733-x
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45. Machine Learning Models for Predicting Shear Wave Velocity of Soils Z. Mousavi et al. https://doi.org/10.1088/1755-1315/1334/1/012039
46. Scale and spatial distribution assessment of rainfall-induced landslides in a catchment with mountain roads C. Tseng et al. https://doi.org/10.5194/nhess-18-687-2018
47. An Interpretable Uncertainty-Aware Framework for Landslide Susceptibility Mapping Based on Weak Supervision and Probabilistic Inference B. Yu et al. https://doi.org/10.3390/rs18152619
48. A meta model for time-variant reliability analysis of unsaturated slope under rainfall A. Abdullah et al. https://doi.org/10.1108/MLAG-01-2025-0003
49. Spatial Non-Stationarity-Based Landslide Susceptibility Assessment Using PCAMGWR Model Y. Li et al. https://doi.org/10.3390/w14060881
50. Research on landslide hazard assessment in data-deficient areas: a case study of Tumen City, China X. Li et al. https://doi.org/10.1007/s11600-023-01057-w
51. 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
52. Artificial neural network for landslide vulnerability mapping in Leitimur Peninsula Ambon Island F. Puturuhu et al. https://doi.org/10.1088/1755-1315/989/1/012013
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54. A review of recent earthquake-induced landslides on the Tibetan Plateau B. Zhao et al. https://doi.org/10.1016/j.earscirev.2023.104534
55. Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms M. Shafapourtehrany et al. https://doi.org/10.3390/rs15184501
56. Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models C. Polykretis & C. Chalkias https://doi.org/10.1007/s11069-018-3299-7
57. Landslide Susceptibility Assessment at the Basin Scale for Rainfall- and Earthquake-Triggered Shallow Slides C. Gordo et al. https://doi.org/10.3390/geosciences9060268
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