61 citations as recorded by crossref.

1. A rockslide-induced debris flow caused the catastrophic 2024 Wayanad disaster in Kerala, India P. Roy et al. https://doi.org/10.1007/s10346-025-02484-0
2. Integrating machine learning and physics-based slope stability through Bayesian geostatistics: An uncertainty-aware framework for landslide prediction A. Achu et al. https://doi.org/10.1016/j.gsf.2026.102402
3. Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars C. Li et al. https://doi.org/10.5194/nhess-22-2317-2022
4. Métodos estadísticos para la evaluación de la susceptibilidad por movimientos en masa E. Aristizábal-Giraldo et al. https://doi.org/10.22430/22565337.1247
5. LogISEL: an interpretable ensemble framework for real-time landslide prediction in the Indian Himalayas M. Chand et al. https://doi.org/10.1186/s40562-026-00477-y
6. Integrated Geotechnical and Structural Resilience: A 25-Year Case Study of Slope Stabilization and Infrastructure Rehabilitation in Madeira Island R. Alves & S. Lousada https://doi.org/10.3390/buildings16132697
7. Automatic identification of buildings vulnerable to debris flows in Sichuan Province, China, by GIS analysis and Deep Encoding Network methods L. Wei et al. https://doi.org/10.1111/jfr3.12830
8. Predisposition to Mass Movements on Railway Slopes: Insights from Field Data on Geotechnical and Pluviometric Influences P. Campos et al. https://doi.org/10.3390/infrastructures9100168
9. Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology E. Canli et al. https://doi.org/10.5194/nhess-18-2183-2018
10. Estimation of rainfall threshold for the early warning of shallow landslides along National Highway-10 in Darjeeling Himalayas P. Mandal & S. Sarkar https://doi.org/10.1007/s11069-020-04407-9
11. Physical Model Test on the Interface of Loess Fill Slope W. Tan et al. https://doi.org/10.3390/land11081372
12. Cluster landslides and associated damage in the Dima Hasao district of Assam, India due to heavy rainfall in May 2022 P. Roy et al. https://doi.org/10.1007/s10346-022-01977-6
13. Introducing SlideforMAP: a probabilistic finite slope approach for modelling shallow-landslide probability in forested situations F. van Zadelhoff et al. https://doi.org/10.5194/nhess-22-2611-2022
14. Towards Sustainable Heritage Conservation: A Hybrid Landslide Susceptibility Mapping Framework in Japan’s UNESCO Mountain Villages A. Bassem et al. https://doi.org/10.3390/su18010237
15. Using the meteorological early warning model to improve the prediction accuracy of water damage geological disasters around pipelines in mountainous areas B. Hong et al. https://doi.org/10.1016/j.scitotenv.2023.164334
16. Sensitivity analysis of regional rainfall-induced landslide based on UAV photogrammetry and LSTM neural network L. Zhao et al. https://doi.org/10.1007/s11629-023-7991-z
17. A new landslide inventory and improved susceptibility model for northeastern Pennsylvania B. Karimi et al. https://doi.org/10.1306/eg.09191919008
18. Development and validation of the terrain stability model for assessing landslide instability during heavy rain infiltration A. Gutiérrez-Martín et al. https://doi.org/10.5194/nhess-19-721-2019
19. A GIS-based probabilistic analysis model for rainfall-induced shallow landslides in mountainous areas C. Li et al. https://doi.org/10.1007/s12665-022-10562-y
20. Stability Assessment of Shallow Soil Landslide and Activating Rainfall Threshold Z. Tan et al. https://doi.org/10.1061/NHREFO.NHENG-1961
21. Topographical and geological variation of effective rainfall for debris-flow occurrence from a large-scale perspective S. Liu et al. https://doi.org/10.1016/j.geomorph.2020.107134
22. A systematic review of prediction methods for emergency management D. Huang et al. https://doi.org/10.1016/j.ijdrr.2021.102412
23. Incorporating compound temporal precipitation dynamics to enhance landslide susceptibility modeling J. Wang et al. https://doi.org/10.1038/s44304-026-00181-z
24. Improved Method of Defining Rainfall Intensity and Duration Thresholds for Shallow Landslides Based on TRIGRS S. Zhang et al. https://doi.org/10.3390/w14040524
25. IED-GCN: An Internal and External Decoupled Graph Convolutional Network for Landslide Susceptibility Assessment Y. Li et al. https://doi.org/10.1109/TGRS.2025.3595205
26. Evaluation of critical factors for typhoon-triggered landslide and subsequent debris flow in the Philippines using physically based models J. Christian Gaviola et al. https://doi.org/10.1051/e3sconf/202560401001
27. Risk-averse rehabilitation decision framework for roadside slopes vulnerable to rainfall-induced geohazards A. Baral & M. Shahandashti https://doi.org/10.1186/s43065-022-00057-2
28. Probabilistic and physically-based modelling of rainfall-induced landslide susceptibility using integrated GIS-FORM algorithm H. Cui et al. https://doi.org/10.1007/s10346-024-02226-8
29. Mechanisms of landslides induced by extreme rainfall in folded mountains: Case study of Zhenba, July 1, 2023 W. Zheng et al. https://doi.org/10.1016/j.geomorph.2025.110115
30. Effects of hysteretic soil water characteristic curves on the hydromechanical behaviour Y. Yang & H. Yeh https://doi.org/10.1007/s12040-023-02146-6
31. Constructing physical-based rainfall landslides prediction model: insights from rainfall threshold curves database of slope units K. Wang et al. https://doi.org/10.5194/nhess-26-2367-2026
32. Integrating local pore water pressure monitoring in territorial early warning systems for weather-induced landslides G. Pecoraro & M. Calvello https://doi.org/10.1007/s10346-020-01599-w
33. A GIS-physically-based emergency methodology for predicting rainfall-induced shallow landslide zonation A. Gutiérrez-Martín https://doi.org/10.1016/j.geomorph.2020.107121
34. Quantify the effect of antecedent effective precipitation on rainfall intensity-duration threshold of debris flow S. Zhang et al. https://doi.org/10.1007/s10346-023-02066-y
35. Model test: Infrasonic features of porous soil masses as applied to landslide monitoring S. Zhang et al. https://doi.org/10.1016/j.enggeo.2019.105454
36. Research on the Impact of Regional-Scale Soil Mechanics Parameter Disturbances on Rainfall Landslides Warning K. Wang et al. https://doi.org/10.3390/geosciences15120449
37. Effects of plant roots on soil shear strength and shallow landslide proneness in an area of northern Italian Apennines M. Bordoni et al. https://doi.org/10.1007/s10064-020-01783-1
38. Rainfall-Adaptive Landslide Monitoring Framework Integrating FLAC3D Numerical Simulation and Multi-Sensor Optimization: A Case Study in the Tianshan Mountains X. Dai et al. https://doi.org/10.3390/s25175433
39. Evaluate the Probability of Failure in Rainfall-Induced Landslides Using a Fuzzy Point Estimate Method Y. Yang & H. Yeh https://doi.org/10.1155/2019/3587989
40. Space-time modeling of rainfall-induced landslides via Physics-Informed Neural Networks: Moving toward a new paradigm for regional early warnings F. Xu et al. https://doi.org/10.1016/j.enggeo.2026.108989
41. Applicability and performance of deterministic and probabilistic physically based landslide modeling in a data-scarce environment of the Colombian Andes R. Marin et al. https://doi.org/10.1016/j.jsames.2021.103175
42. Analysis of Slope Failure Behaviour Based on Real-Time Measurement Using the x–MR Method S. Park et al. https://doi.org/10.3390/jmse7100360
43. GIS-Based Data Integration Approach for Rainfall-Induced Slope Failure Susceptibility Mapping in Clayey Soils A. Baral et al. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000478
44. Identifying critical combination of roadside slopes susceptible to rainfall-induced failures A. Baral & S. Shahandashti https://doi.org/10.1007/s11069-022-05343-6
45. Prediction of the instability probability for rainfall induced landslides: the effect of morphological differences in geomorphology within mapping units K. Wang et al. https://doi.org/10.1007/s11629-022-7789-4
46. Rainfall-induced landslides assessment in the Fengjie County, Three-Gorge reservoir area, China K. Wang & S. Zhang https://doi.org/10.1007/s11069-021-04691-z
47. Landslide susceptibility assessment in the Central Yunnan Plateau by assembling optimized statistical and machine learning models J. Chen et al. https://doi.org/10.1186/s40677-026-00380-w
48. Physically-based landslide susceptibility analysis using Monte Carlo simulation in a tropical mountain basin R. Marin & Á. Mattos https://doi.org/10.1080/17499518.2019.1633582
49. Landslide susceptibility mapping in the Adwa Volcanic Mountain Plugs, Northern Ethiopia: a comparative analysis of frequency ratio and analytical hierarchy process methods G. Berhane et al. https://doi.org/10.1080/19475705.2023.2281244
50. Development of environmental contours from rainfall intensity and duration data for slopes X. Wu et al. https://doi.org/10.1007/s11069-022-05709-w
51. Major landslides in Kerala, India, during 2018–2020 period: an analysis using rainfall data and debris flow model N. Jain et al. https://doi.org/10.1007/s10346-021-01746-x
52. A grid-based physical model to analyze the stability of slope unit S. Zhang et al. https://doi.org/10.1016/j.geomorph.2021.107887
53. A physics-based model to derive rainfall intensity-duration threshold for debris flow S. Zhang et al. https://doi.org/10.1016/j.geomorph.2019.106930
54. Quantification of the mechanical reinforcement of roots of olive trees and their contribution to shallow slope stability M. Bordoni et al. https://doi.org/10.1016/j.catena.2026.109929
55. GIS-based prediction method of shallow landslides induced by heavy rainfall in large mountainous areas X. Luo et al. https://doi.org/10.1007/s11629-023-8535-2
56. A Scientometric Analysis of Predicting Methods for Identifying the Environmental Risks Caused by Landslides Y. Zou & C. Zheng https://doi.org/10.3390/app12094333
57. Probabilistic modeling of shallow landslide initiation using regional scale random fields J. Lizárraga & G. Buscarnera https://doi.org/10.1007/s10346-020-01438-y
58. A Regional-Scale Early Warning System for Rainfall-Induced Shallow Landslides Based on the Outputs of a Physically Based Model: Application to Cili County, China W. Lin et al. https://doi.org/10.3390/w18020168
59. A hydrology-process based method for correlating debris flow density to rainfall parameters and its application on debris flow prediction K. Long et al. https://doi.org/10.1016/j.jhydrol.2020.125124
60. Influence of soil mechanical and hydraulic parameters on the definition of rainfall intensity and duration thresholds based on Transient rainfall infiltration and grid-based regional slope-stability model (TRIGRS) S. Zhang et al. https://doi.org/10.3389/feart.2022.971655
61. Machine learning techniques on spatio-temporal data for landslide susceptibility assessment at Dieng Mountainous Region, Banjarnegara district, Central Java, Indonesia Y. Susena et al. https://doi.org/10.1007/s11069-025-07136-z