104 citations as recorded by crossref.

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4. Technical Note: An operational landslide early warning system at regional scale based on space–time-variable rainfall thresholds S. Segoni et al. https://doi.org/10.5194/nhess-15-853-2015
5. Fusion of SAR Interferometry and Polarimetry Methods for Landslide Reactivation Study, the Bureya River (Russia) Event Case Study V. Bondur et al. https://doi.org/10.3390/rs13245136
6. Monitoring the risk of a tailings dam collapse through spectral analysis of satellite InSAR time-series data S. Das et al. https://doi.org/10.1007/s00477-024-02713-3
7. Development of an algorithm for automatic elaboration, representation and dissemination of landslide monitoring data A. Wrzesniak & D. Giordan https://doi.org/10.1080/19475705.2017.1392369
8. The Rio de Janeiro early warning system for rainfall-induced landslides: Analysis of performance for the years 2010–2013 M. Calvello et al. https://doi.org/10.1016/j.ijdrr.2014.10.005
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10. Geomorphological analysis and early warning systems for landslide risk mitigation in Nepalese mid-hills P. Thapa et al. https://doi.org/10.1007/s11069-023-05929-8
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12. A generalized early warning criterion for the landslide risk assessment: deformation probability index (DPI) J. Zhang et al. https://doi.org/10.1007/s11440-023-02199-3
13. Model and criteria for slope monitoring and early warning in open-pit mines using ground-based real aperture radar H. Ding et al. https://doi.org/10.1007/s12665-024-11863-0
14. A Fast Deploying Monitoring and Real-Time Early Warning System for the Baige Landslide in Tibet, China Y. Wu et al. https://doi.org/10.3390/s20226619
15. Analyzing the effects of critical active waveguide parameters on acoustic emission characteristics for soil slope deformation monitoring D. Kumar et al. https://doi.org/10.1007/s10064-024-03857-w
16. Landslide displacement prediction based on Variational mode decomposition and MIC-GWO-LSTM model Z. Taorui et al. https://doi.org/10.1007/s00477-021-02145-3
17. Low-Cost Sensor-Based and LoRaWAN Opportunities for Landslide Monitoring Systems on IoT Platform: A Review S. Bagwari et al. https://doi.org/10.1109/ACCESS.2021.3137841
18. Adaptive interval prediction method for step-like landslide displacement with dynamic switching between different deformation states Y. Zhang et al. https://doi.org/10.1007/s10064-023-03418-7
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25. Remote Sensing Precursors Analysis for Giant Landslides H. Lan et al. https://doi.org/10.3390/rs14174399
26. Definition and performance of a threshold-based regional early warning model for rainfall-induced landslides L. Piciullo et al. https://doi.org/10.1007/s10346-016-0750-2
27. Application of the method for prediction of the failure location and time based on monitoring of a slope using synthetic aperture radar Y. Zhang et al. https://doi.org/10.1007/s12665-021-09989-6
28. Experiences from site-specific landslide early warning systems C. Michoud et al. https://doi.org/10.5194/nhess-13-2659-2013
29. Sinkhole monitoring and early warning: An experimental and successful GB-InSAR application E. Intrieri et al. https://doi.org/10.1016/j.geomorph.2015.04.018
30. Observation Method in the Control of Stacker Capacity Under Landslide Hazard – A Case Study M. Bąk & I. Bagińska https://doi.org/10.2478/sgem-2022-0013
31. Brief Communication: On the rapid and efficient monitoring results dissemination in landslide emergency scenarios: the Mont de La Saxe case study D. Giordan et al. https://doi.org/10.5194/nhess-15-2009-2015
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35. Assessing the performance of regional landslide early warning models: the EDuMaP method M. Calvello & L. Piciullo https://doi.org/10.5194/nhess-16-103-2016
36. Rapid characterisation of landslide heterogeneity using unsupervised classification of electrical resistivity and seismic refraction surveys J. Whiteley et al. https://doi.org/10.1016/j.enggeo.2021.106189
37. Landslide Susceptibility Mapping of Central and Western Greece, Combining NGI and WoE Methods, with Remote Sensing and Ground Truth Data C. Kontoes et al. https://doi.org/10.3390/land10040402
38. Structural study of a deep-seated rock landslide using seismic noise H. Wang et al. https://doi.org/10.1007/s10064-025-04603-6
39. A web-based platform for automatic and continuous landslide monitoring: The Rotolon (Eastern Italian Alps) case study S. Frigerio et al. https://doi.org/10.1016/j.cageo.2013.10.015
40. Basic features of the predictive tools of early warning systems for water-related natural hazards: examples for shallow landslides R. Greco & L. Pagano https://doi.org/10.5194/nhess-17-2213-2017
41. When hazard avoidance is not an option: lessons learned from monitoring the postdisaster Oso landslide, USA M. Reid et al. https://doi.org/10.1007/s10346-021-01686-6
42. Application of advanced geophysical and photogrammetric methods in modeling and monitoring an active landslide: A case study from Podhale (Southern Poland) M. Kamiński https://doi.org/10.1016/j.enggeo.2025.108484
43. Creep response in shear of clayey geo-materials under saturated and unsaturated conditions N. Mohd Nazer et al. https://doi.org/10.1051/e3sconf/20160914023
44. GB-InSAR monitoring of slope deformations in a mountainous area affected by debris flow events W. Frodella et al. https://doi.org/10.5194/nhess-17-1779-2017
45. Application of the terrestrial laser scanner in the monitoring of earth structures J. Kogut & E. Pilecka https://doi.org/10.1515/geo-2020-0033
46. Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method C. Zhou et al. https://doi.org/10.1007/s10346-018-1022-0
47. Enhancing the reliability of landslide early warning systems by machine learning H. Thirugnanam et al. https://doi.org/10.1007/s10346-020-01453-z
48. Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting A. van Natijne et al. https://doi.org/10.3390/s20051425
49. ADVICE: A New Approach for Near-Real-Time Monitoring of Surface Displacements in Landslide Hazard Scenarios P. Allasia et al. https://doi.org/10.3390/s130708285
50. Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application E. Intrieri et al. https://doi.org/10.5194/nhess-17-1713-2017
51. Physically based and distributed rainfall intensity and duration thresholds for shallow landslides R. Marin https://doi.org/10.1007/s10346-020-01481-9
52. Critical threshold mining of landslide deformation and intelligent early-warning methods based on multi-factor fusion D. Liu et al. https://doi.org/10.1007/s10064-024-03841-4
53. Using Complementary Ensemble Empirical Mode Decomposition and Gated Recurrent Unit to Predict Landslide Displacements in Dam Reservoir B. Yang et al. https://doi.org/10.3390/s22041320
54. Satellite Interferometry as a Tool for Early Warning and Aiding Decision Making in an Open-Pit Mine E. Intrieri et al. https://doi.org/10.1109/JSTARS.2019.2953339
55. The First International Workshop on Warning Criteria for Active Slides: technical issues, problems and solutions for managing early warning systems C. Cloutier et al. https://doi.org/10.1007/s10346-014-0530-9
56. Safety monitoring and effect analysis of fracturing body on the right bank of Pubugou reservoir head in China based on space-ground-body monitoring mode H. Liang et al. https://doi.org/10.1007/s10346-024-02230-y
57. Double-threshold validation tool (DTVT): From landslide hazard maps to operational early warning systems N. Nocentini et al. https://doi.org/10.1016/j.ijdrr.2025.105786
58. Monitoring strategies for local landslide early warning systems G. Pecoraro et al. https://doi.org/10.1007/s10346-018-1068-z
59. Hydraulic response and stability of a reservoir slope with landslide potential under the combined effect of rainfall and water level fluctuation M. Chen et al. https://doi.org/10.1007/s12665-020-09279-7
60. Systèmes d’alerte précoce pour les aléas naturels et environnementaux : virage ou mirage technologique ? D. Didier et al. https://doi.org/10.7202/1042922ar
61. Monitoring and prediction in early warning systems for rapid mass movements M. Stähli et al. https://doi.org/10.5194/nhess-15-905-2015
62. A Method for Predicting Landslides Based on Micro-Deformation Monitoring Radar Data W. Tan et al. https://doi.org/10.3390/rs15030826
63. Utilizing crowdsourcing to enhance the mitigation and management of landslides C. Choi et al. https://doi.org/10.1007/s10346-018-1034-9
64. A new early warning criterion for landslides movement assessment: Deformation Standardized Anomaly Index J. Zhang et al. https://doi.org/10.1007/s10064-024-03672-3
65. Quantifying the effectiveness of early warning systems for natural hazards M. Sättele et al. https://doi.org/10.5194/nhess-16-149-2016
66. Geological monitoring networks for risk management close to large rock cliffs: the case history of Gallivaggio and Cataeggio in the italian Alps L. Dei Cas et al. https://doi.org/10.5194/gh-76-85-2021
67. A simple method to help determine landslide susceptibility from spaceborne InSAR data: the Montescaglioso case study T. Carlà et al. https://doi.org/10.1007/s12665-016-6308-8
68. Long-term geoelectrical monitoring of landslides in natural and engineered slopes J. Chambers et al. https://doi.org/10.1190/tle41110768.1
69. Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology E. Canli et al. https://doi.org/10.5194/nhess-18-2183-2018
70. A statistical-based approach for determining the intensity of unrest phases at Stromboli volcano (Southern Italy) using one-step-ahead forecasts of displacement time series T. Carlà et al. https://doi.org/10.1007/s11069-016-2451-5
71. Development of community-based landslide early warning system in the earthquake-affected areas of Nepal Himalaya P. Thapa & B. Adhikari https://doi.org/10.1007/s11629-019-5586-5
72. Alert threshold assessment based on equivalent displacements for the identification of potentially critical landslide events A. Valletta et al. https://doi.org/10.1007/s11069-022-05606-2
73. Displacement residuals reveal landslide regime shifts L. Nava et al. https://doi.org/10.1007/s10346-024-02353-2
74. Towards establishing rainfall thresholds for a real-time landslide early warning system in Sikkim, India G. Harilal et al. https://doi.org/10.1007/s10346-019-01244-1
75. Assessing two methods of defining rainfall intensity and duration thresholds for shallow landslides in data-scarce catchments of the Colombian Andean Mountains R. Marin et al. https://doi.org/10.1016/j.catena.2021.105563
76. Landslide forecasting and factors influencing predictability E. Intrieri & G. Gigli https://doi.org/10.5194/nhess-16-2501-2016
77. High-resolution geophysical monitoring of moisture accumulation preceding slope movement—a path to improved early warning A. Watlet et al. https://doi.org/10.1088/1748-9326/ad8fbe
78. LandAware: a new international network on Landslide Early Warning Systems M. Calvello et al. https://doi.org/10.1007/s10346-020-01548-7
79. A new early warning Criterion for assessing landslide risk Y. Du et al. https://doi.org/10.1007/s11069-022-05687-z
80. On the monitoring and early-warning of brittle slope failures in hard rock masses: Examples from an open-pit mine T. Carlà et al. https://doi.org/10.1016/j.enggeo.2017.08.007
81. Data-Driven Combination-Interval Prediction for Landslide Displacement Based on Copula and VMD-WOA-KELM Method L. Li et al. https://doi.org/10.1007/s12583-021-1555-3
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