36 citations as recorded by crossref.

1. Physically-based rainfall-induced landslide thresholds for the Tianshui area of Loess Plateau, China by TRIGRS model S. Ma et al. 10.1016/j.catena.2023.107499
2. Development and validation of the terrain stability model for assessing landslide instability during heavy rain infiltration A. Gutiérrez-Martín et al. 10.5194/nhess-19-721-2019
3. Spatial modeling of land subsidence using machine learning models and statistical methods M. Sekkeravani et al. 10.1007/s11356-021-18037-6
4. Stochastic seepage and slope stability analysis using vine-copula based multivariate random field approach: Consideration to non-Gaussian spatial and cross-dependence structure of hydraulic parameters A. Sharma et al. 10.1016/j.compgeo.2020.103918
5. Double-index rainfall warning and probabilistic physically based model for fast-moving landslide hazard analysis in subtropical-typhoon area T. Zeng et al. 10.1007/s10346-023-02187-4
6. Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models H. Oh et al. 10.3390/app9061248
7. Novel Bayesian framework for calibration of spatially distributed physical-based landslide prediction models. I. Depina et al. 10.1016/j.compgeo.2020.103660
8. A Regional-Scale Landslide Warning System Based on 20 Years of Operational Experience S. Segoni et al. 10.3390/w10101297
9. Physically-based landslide susceptibility analysis using Monte Carlo simulation in a tropical mountain basin R. Marin & Á. Mattos 10.1080/17499518.2019.1633582
10. Recent advancements of landslide hydrology R. Greco et al. 10.1002/wat2.1675
11. A GIS-physically-based emergency methodology for predicting rainfall-induced shallow landslide zonation A. Gutiérrez-Martín 10.1016/j.geomorph.2020.107121
12. Shallow Landslide Susceptibility Models Based on Artificial Neural Networks Considering the Factor Selection Method and Various Non-Linear Activation Functions D. Lee et al. 10.3390/rs12071194
13. Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques Y. Li & W. Chen 10.3390/w12010113
14. Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico R. Baum et al. 10.5194/nhess-24-1579-2024
15. Assessment of Landslide Susceptibility of the Wiśnickie Foothills Mts. (The Flysch Carpathians, Poland) Using Selected Machine Learning Algorithms T. Zydroń et al. 10.3389/feart.2022.872192
16. Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling A. Felsberg et al. 10.5194/nhess-23-3805-2023
17. Improving categorical and continuous accuracy of precipitation forecasts by integrating Empirical Quantile Mapping and Bernoulli-Gamma-Gaussian distribution L. Li et al. 10.1016/j.atmosres.2023.107133
18. Temporal prediction modeling for rainfall-induced shallow landslide hazards using extreme value distribution J. Lee et al. 10.1007/s10346-020-01502-7
19. Integrating local pore water pressure monitoring in territorial early warning systems for weather-induced landslides G. Pecoraro & M. Calvello 10.1007/s10346-020-01599-w
20. High-Resolution Lidar-Derived DEM for Landslide Susceptibility Assessment Using AHP and Fuzzy Logic in Serdang, Malaysia J. Okoli et al. 10.3390/geosciences13020034
21. 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. 10.3390/w15030443
22. A Tool for the Automatic Aggregation and Validation of the Results of Physically Based Distributed Slope Stability Models M. Bulzinetti et al. 10.3390/w13172313
23. A New Approach Based on Balancing Composite Motion Optimization and Deep Neural Networks for Spatial Prediction of Landslides at Tropical Cyclone Areas T. Tuan et al. 10.1109/ACCESS.2023.3291411
24. Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars C. Li et al. 10.5194/nhess-22-2317-2022
25. Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scale M. Bordoni et al. 10.1007/s10346-020-01592-3
26. Preface: Landslide early warning systems: monitoring systems, rainfall thresholds, warning models, performance evaluation and risk perception S. Segoni et al. 10.5194/nhess-18-3179-2018
27. Landslides in the Mountain Region of Rio de Janeiro: A Proposal for the Semi-Automated Definition of Multiple Rainfall Thresholds A. Rosi et al. 10.3390/geosciences9050203
28. Global Sensitivity Analysis of Groundwater Related Dike Stability under Extreme Loading Conditions T. van Woerkom et al. 10.3390/w13213041
29. Local-Scale Weather Forecasts over a Complex Terrain in an Early Warning Framework: Performance Analysis for the Val d’Agri (Southern Italy) Case Study G. Giunta et al. 10.1155/2022/2179246
30. Metodologías para la evaluación de la amenaza por movimientos en masa como parte de los estudios básico de amenaza: caso de estudio municipio de Andes, Antioquia, Colombia E. Aristizábal et al. 10.18273/revbol.v44n3-2022009
31. Optimization of rainfall thresholds for landslide early warning through false alarm reduction and a multi-source validation N. Nocentini et al. 10.1007/s10346-023-02176-7
32. Assessment of the capability of modern reanalyses to simulate precipitation in warm months using adjusted radar precipitation V. Bližňák et al. 10.1016/j.ejrh.2022.101121
33. Triggering conditions and propagation of the December 2019 Palma Campania landslide: Implications for residual hazard estimation at recurrent landslide sites C. Sepe et al. 10.1016/j.enggeo.2023.107177
34. Brief communication: Using averaged soil moisture estimates to improve the performances of a regional-scale landslide early warning system S. Segoni et al. 10.5194/nhess-18-807-2018
35. Rainfall threshold calculation for debris flow early warning in areas with scarcity of data H. Pan et al. 10.5194/nhess-18-1395-2018
36. Landslide susceptibility mapping using state-of-the-art machine learning ensembles B. Pham et al. 10.1080/10106049.2021.1914746