83 citations as recorded by crossref.

1. Interpretable Machine Learning for Explaining and Predicting Collapse Hazards in the Changbai Mountain Region X. He et al.
2. Shallow Landslides and Rockfalls Velocity Assessment at Regional Scale: A Methodology Based on a Morphometric Approach A. Marinelli et al.
3. Root Reinforcement in Slope Stability Models: A Review E. Masi et al.
4. Regional seismic landslide susceptibility assessment considering the rock mass strength heterogeneity S. Chen et al.
5. Double-threshold validation tool (DTVT): From landslide hazard maps to operational early warning systems N. Nocentini et al.
6. Analysis of Soil–Water Characteristics and Stability Evolution of Rainfall-Induced Landslide: A Case of the Siwan Village Landslide H. Wen et al.
7. Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models E. Nohani et al.
8. Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: The Case of Rio de Janeiro, Brazil A. Getirana et al.
9. Geotechnical and geophysical property models of soil-covered slopes prone to landsliding. The case study of the Ischia Island (southern Italy) R. Di Maio et al.
10. Prediction of the instability probability for rainfall induced landslides: the effect of morphological differences in geomorphology within mapping units K. Wang et al.
11. Integrated approach for landslide hazard assessment in the High City of Antananarivo, Madagascar (UNESCO tentative site) W. Frodella et al.
12. The influence of postfire root strength decay on shallow landslide susceptibility in western Oregon D. Sousa et al.
13. Effects of coupled hydro-mechanical model considering dual-phase fluid flow on potential for shallow landslides at a regional scale S. Kang & B. Kim
14. Landslides in the Mountain Region of Rio de Janeiro: A Proposal for the Semi-Automated Definition of Multiple Rainfall Thresholds A. Rosi et al.
15. Multiseasonal probabilistic slope stability analysis of a large area of unsaturated pyroclastic soils S. Cuomo et al.
16. Slope stability modelling of shallow landslides at a regional scale N. Brilli et al.
17. Characterizing the Distribution Pattern and a Physically Based Susceptibility Assessment of Shallow Landslides Triggered by the 2019 Heavy Rainfall Event in Longchuan County, Guangdong Province, China S. Ma et al.
18. Hong Kong’s landslip warning system—40 years of progress V. Kong et al.
19. A framework for temporal and spatial rockfall early warning using micro-seismic monitoring L. Feng et al.
20. State-of-the-art: parametrization of hydrological and mechanical reinforcement effects of vegetation in slope stability models for shallow landslides A. DiBiagio et al.
21. Quantifying effectiveness of trees for landslide erosion control R. Spiekermann et al.
22. Assessing limit equilibrium method approach and mapping critical areas for slope stability analysis in Serra do Mar Paranaense—Brazil A. Acevedo et al.
23. Constraining landslide frequency across the United States to inform county-level risk reduction L. Luna et al.
24. A Tool for the Automatic Aggregation and Validation of the Results of Physically Based Distributed Slope Stability Models M. Bulzinetti et al.
25. A systematic review on rainfall thresholds for landslides occurrence F. Gonzalez et al.
26. Landslide Susceptibility Modeling Using Integrated Ensemble Weights of Evidence with Logistic Regression and Random Forest Models W. Chen et al.
27. Accelerating Effect of Vegetation on the Instability of Rainfall-Induced Shallow Landslides J. Zhang et al.
28. Decoding vegetation's role in landslide susceptibility mapping: An integrated review of techniques and future directions Y. Li & W. Duan
29. A Sentinel-1 based hot-spot analysis: landslide mapping in north-western Italy L. Solari et al.
30. Comparing physical and statistical landslide susceptibility models at the scale of individual trees R. Spiekermann et al.
31. Introducing SlideforMAP: a probabilistic finite slope approach for modelling shallow-landslide probability in forested situations F. van Zadelhoff et al.
32. Near-real-time seismic monitoring improves deep-seated landslides early warning, Jiuxianping, China L. Feng et al.
33. Developing a Dynamic Web-GIS Based Landslide Early Warning System for the Chittagong Metropolitan Area, Bangladesh B. Ahmed et al.
34. Rainfall-induced landslide prediction models, part ii: deterministic physical and phenomenologically models K. Ebrahim et al.
35. Reinforcement learning for energy optimization in IoT based landslide early warning systems S. Arslan et al.
36. Landslide susceptibility mapping using GIS along the Niš-North Macedonia highway D. Tešić
37. Regional Landslide Hazard Assessment Using Extreme Value Analysis and a Probabilistic Physically Based Approach H. Park et al.
38. Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy S. Segoni et al.
39. Integrating Physical and Machine Learning Models for Enhanced Landslide Prediction in Data-Scarce Environments H. Al-Najjar et al.
40. Preface: Landslide early warning systems: monitoring systems, rainfall thresholds, warning models, performance evaluation and risk perception S. Segoni et al.
41. Effects of roots cohesion on regional distributed slope stability modelling E. Masi et al.
42. Towards a National-Scale Dataset of Geotechnical and Hydrological Soil Parameters for Shallow Landslide Modeling P. Vannocci et al.
43. Reconstruction of surface deformation characteristics in alpine canyons under shadow conditions Z. Gu & X. Yao
44. An innovative partition method for predicting shallow landslides by combining the slope stability analysis with a dynamic neural network model P. Huang
45. Incorporating compound temporal precipitation dynamics to enhance landslide susceptibility modeling J. Wang et al.
46. X-SLIP: A SLIP-based multi-approach algorithm to predict the spatial–temporal triggering of rainfall-induced shallow landslides over large areas M. Placido Antonio Gatto & L. Montrasio
47. Optimization of rainfall thresholds for landslide early warning through false alarm reduction and a multi-source validation N. Nocentini et al.
48. Improving Spatial Landslide Prediction with 3D Slope Stability Analysis and Genetic Algorithm Optimization: Application to the Oltrepò Pavese N. Palazzolo et al.
49. Integration of Satellite Interferometric Data in Civil Protection Strategies for Landslide Studies at a Regional Scale S. Bianchini et al.
50. Sentinel-1-based monitoring services at regional scale in Italy: State of the art and main findings P. Confuorto et al.
51. A comparative study of different machine learning methods coupled with GIS for landslide susceptibility assessment: a case study of N’fis basin, Marrakesh High Atlas (Morocco) H. Ait Naceur et al.
52. Landslides Triggered by the 2016 Heavy Rainfall Event in Sanming, Fujian Province: Distribution Pattern Analysis and Spatio-Temporal Susceptibility Assessment S. Ma et al.
53. Applicability and performance of deterministic and probabilistic physically based landslide modeling in a data-scarce environment of the Colombian Andes R. Marin et al.
54. Surface temperature controls the pattern of post-earthquake landslide activity M. Loche et al.
55. Developing Hydro-Meteorological Thresholds for Shallow Landslide Initiation and Early Warning B. Mirus et al.
56. Landslide susceptibility mapping with GIS in high mountain area of Nepal: a comparison of four methods P. Gautam et al.
57. Exploring a landslide inventory created by automated web data mining: the case of Italy R. Franceschini et al.
58. Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility P. Lima et al.
59. Probabilistic analysis of rainfall-induced shallow landslide susceptibility using a physically based model and the bootstrap method I. Hwang et al.
60. Establishing a shallow-landslide prediction method by using machine-learning techniques based on the physics-based calculation of soil slope stability P. Huang
61. A Landslide Probability Model Based on a Long-Term Landslide Inventory and Rainfall Factors C. Wu & Y. Yeh
62. A grid-based physical model to analyze the stability of slope unit S. Zhang et al.
63. Development of rainfall threshold equation and bayesian probabilistic analysis for landslide prediction: A case study of Shimla, Northwestern Himalaya, India J. Singh et al.
64. Definition of 3D rainfall thresholds to increase operative landslide early warning system performances A. Rosi et al.
65. Loess landslides detection via a partially supervised learning and improved Mask-RCNN with multi-source remote sensing data J. Wang et al.
66. Event-based rainfall-induced landslide inventories and rainfall thresholds for Malawi P. Niyokwiringirwa et al.
67. Shallow landslides in Tuscany (Central Italy) triggered by the November 2023 heavy rainfall event E. Masi et al.
68. Towards establishing rainfall thresholds for a real-time landslide early warning system in Sikkim, India G. Harilal et al.
69. Predicting Landslides Susceptible Zones in the Lesser Himalayas by Ensemble of Per Pixel and Object-Based Models U. Sur et al.
70. An urban drainage scheme for large-scale flood models A. Getirana et al.
71. Department of Earth Sciences, University of Florence N. Casagli & V. Tofani
72. Shallow landslides and vegetation at the catchment scale: A perspective C. Phillips et al.
73. Spatial Prediction of Landslide Susceptibility Based on GIS and Discriminant Functions G. Wang et al.
74. Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia A. Youssef & H. Pourghasemi
75. Satellite interferometric data for landslide intensity evaluation in mountainous regions L. Solari et al.
76. Lithologically-constrained, machine learning-based temporal landslide prediction models using rainfall time series for the Benguet First Engineering District, Philippines J. Abuda et al.
77. Insight from a Physical-Based Model for the Triggering Mechanism of Loess Landslides Induced by the 2013 Tianshui Heavy Rainfall Event S. Ma et al.
78. Conventional data-driven landslide susceptibility models may only tell us half of the story: Potential underestimation of landslide impact areas depending on the modeling design P. Lima et al.
79. Shifting from traditional landslide occurrence modeling to scenario estimation with a “glass-box” machine learning F. Caleca et al.
80. Geotechnical and hydrological characterization of hillslope deposits for regional landslide prediction modeling G. Bicocchi et al.
81. Mapping Pluvial Flood-Induced Damages with Multi-Sensor Optical Remote Sensing: A Transferable Approach A. Cerbelaud et al.
82. Determination of GIS-Based Landslide Susceptibility and Ground Dynamics with Geophysical Measurements and Machine Learning Algorithms H. Dindar & Ç. Alevkayalı
83. Quantifying the influence of individual trees on slope stability at landscape scale R. Spiekermann et al.