225 citations as recorded by crossref.

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3. Hydrometeorological disasters in urban areas of Costa Rica, Central America A. Quesada-Román et al. 10.1080/17477891.2020.1791034
4. Developing a landslide vulnerability assessment for the national road network in Laos G. Hearn & P. Pongpanya 10.1144/qjegh2020-110
5. Different Approaches to Use Morphometric Attributes in Landslide Susceptibility Mapping Based on Meso-Scale Spatial Units: A Case Study in Rio de Janeiro (Brazil) V. Canavesi et al. 10.3390/rs12111826
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14. New global characterisation of landslide exposure R. Emberson et al. 10.5194/nhess-20-3413-2020
15. Economic landslide susceptibility under a socio-economic perspective: an application to Umbria Region (Central Italy) M. Donnini et al. 10.1007/s10037-020-00143-6
16. Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models N. Prakash et al. 10.3390/rs12030346
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18. The cost of rapid and haphazard urbanization: lessons learned from the Freetown landslide disaster Y. Cui et al. 10.1007/s10346-019-01167-x
19. Space-time landslide predictive modelling L. Lombardo et al. 10.1016/j.earscirev.2020.103318
20. Effect of basin morphometric parameters on physically-based rainfall thresholds for shallow landslides R. Marin et al. 10.1016/j.enggeo.2020.105855
21. Mapping Landslide Susceptibility Using Machine Learning Algorithms and GIS: A Case Study in Shexian County, Anhui Province, China Z. Wang et al. 10.3390/sym12121954
22. Estimating Rainfall Thresholds for Landslide Occurrence in the Bhutan Himalayas A. Dikshit et al. 10.3390/w11081616
23. Analysis of landslide-induced fatalities and injuries in Bangladesh: 2000-2018 N. Sultana & S. Ricart Casadevall 10.1080/23311886.2020.1737402
24. Machine learning for landslides prevention: a survey Z. Ma et al. 10.1007/s00521-020-05529-8
25. Towards a Transferable Antecedent Rainfall—Susceptibility Threshold Approach for Landsliding . Monsieurs et al. 10.3390/w11112202
26. Landslide precipitation thresholds in Rwanda J. Uwihirwe et al. 10.1007/s10346-020-01457-9
27. Towards a web service for rapid landslide mapping based on Copernicus data F. Albrecht et al. 10.1088/1755-1315/509/1/012002
28. Utilization of google maps for depicting landslide pattern in Indonesia S. Sukristiyanti et al. 10.1088/1755-1315/500/1/012042
29. Probabilistic simulation of entire process of rainfall-induced landslides using random finite element and material point methods with hydro-mechanical coupling X. Liu & Y. Wang 10.1016/j.compgeo.2020.103989
30. Landslide mapping from multi-sensor data through improved change detection-based Markov random field P. Lu et al. 10.1016/j.rse.2019.111235
31. Landslide Characterization Applying Sentinel-1 Images and InSAR Technique: The Muyubao Landslide in the Three Gorges Reservoir Area, China C. Zhou et al. 10.3390/rs12203385
32. Investigation of the dynamic process of the Xinmo landslide using the discrete element method Z. Liu et al. 10.1016/j.compgeo.2020.103561
33. Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms V. Nhu et al. 10.3390/ijerph17082749
34. 3D simulation of Vajont disaster. Part 1: Numerical formulation and validation A. Franci et al. 10.1016/j.enggeo.2020.105854
35. The optimal rainfall thresholds and probabilistic rainfall conditions for a landslide early warning system for Chuncheon, Republic of Korea W. Lee et al. 10.1007/s10346-020-01603-3
36. Implications of landslide inventory in susceptibility modeling along a Himalayan highway corridor, India V. Pandey et al. 10.1080/02723646.2021.1872857
37. Characteristics of failure area and failure mechanism of a landslide in Yingjiang County, Yunnan, China G. Shi et al. 10.1007/s10346-020-01544-x
38. Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability M. Di Napoli et al. 10.1007/s10346-020-01392-9
39. Effect of time and space partitioning strategies of samples on regional landslide susceptibility modelling K. Khanna et al. 10.1007/s10346-021-01627-3
40. Risk at the margins: A natural hazards perspective on the Syrian refugee crisis in Lebanon W. Pollock et al. 10.1016/j.ijdrr.2018.11.026
41. The Spatial and Temporal Influence of Cloud Cover on Satellite-Based Emergency Mapping of Earthquake Disasters T. Robinson et al. 10.1038/s41598-019-49008-0
42. Earthquake‐Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts X. Fan et al. 10.1029/2018RG000626
43. Emergency Disposal Solution for Control of a Giant Landslide and Dammed Lake in Yangtze River, China G. Chen et al. 10.3390/w11091939
44. The Hooskanaden Landslide: historic and recent surge behavior of an active earthflow on the Oregon Coast S. Alberti et al. 10.1007/s10346-020-01466-8
45. Assessing the spatiotemporal impact of climate change on event rainfall characteristics influencing landslide occurrences based on multiple GCM projections in China Q. Lin et al. 10.1007/s10584-020-02750-1
46. Time series analysis and long short-term memory neural network to predict landslide displacement B. Yang et al. 10.1007/s10346-018-01127-x
47. Exploring the rainfall data from satellites to monitor rainfall induced landslides – A case study M. Kumar Thakur et al. 10.1016/j.asr.2020.05.015
48. Characteristics of Rainfall Events Triggering Landslides in Two Climatologically Different Areas: Southern Ecuador and Southern Spain J. Palenzuela Baena et al. 10.3390/hydrology7030045
49. Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping B. Pham et al. 10.1016/j.catena.2020.104805
50. Objectives and main results of “Community Participation for Landslide Disaster Risk Reduction” thematic papers J. Klimeš et al. 10.1007/s10346-019-01246-z
51. 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
52. Spatial variation in the frequency and magnitude of mass movement in a semiarid, complex‐terrain agricultural watershed on the L oess P lateau of C hina T. Zhu et al. 10.1002/ldr.3296
53. Determination of Rainfall Thresholds for Landslide Prediction Using an Algorithm-Based Approach: Case Study in the Darjeeling Himalayas, India T. Teja et al. 10.3390/geosciences9070302
54. InSAR-based detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: An application to Nepal D. Bekaert et al. 10.1016/j.rse.2020.111983
55. Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers B. Pham et al. 10.1080/10106049.2020.1737972
56. Comparing characteristics of rainfall- and earthquake-triggered landslides in the Upper Minjiang catchment, China S. Bai et al. 10.1016/j.enggeo.2020.105518
57. A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images B. Adriano et al. 10.3390/rs12030561
58. An integrated approach for landslide susceptibility–vulnerability–risk assessment of building infrastructures in hilly regions of India A. Singh et al. 10.1007/s10668-020-00804-z
59. Towards A Geo-Hydro-Mechanical Characterization of Landslide Classes: Preliminary Results F. Cotecchia et al. 10.3390/app10227960
60. Application of statistical probabilistic methods in landslide susceptibility assessment in Kurseong and its surrounding area of Darjeeling Himalayan, India: RS-GIS approach A. Saha & S. Saha 10.1007/s10668-020-00783-1
61. Joint detection and classification of rockfalls in a microseismic monitoring network L. Feng et al. 10.1093/gji/ggaa287
62. Preface: Landslide–transport network interactions F. Taylor et al. 10.5194/nhess-20-2585-2020
63. Hazard assessment and mitigation of non-seismically fatal landslides in China F. Zhang et al. 10.1007/s11069-020-04491-x
64. A model for interpreting the deformation mechanism of reservoir landslides in the Three Gorges Reservoir area, China Z. Zou et al. 10.5194/nhess-21-517-2021
65. Landslide risk index map at the municipal scale for Costa Rica A. Quesada-Román 10.1016/j.ijdrr.2021.102144
66. Old but still active: > 18 ka history of rock slope failures affecting a flysch anticline M. Břežný et al. 10.1007/s10346-020-01483-7
67. Spatial modelling of shallow landslide susceptibility: a study from the southern Western Ghats region of Kerala, India. A. Achu et al. 10.1080/19475683.2020.1758207
68. Geotechnical assessment of cut slopes in the landslide-prone Himalayas: rock mass characterization and simulation approach T. Siddique et al. 10.1007/s11069-020-04175-6
69. Thermal Infrared Imagery Integrated with Terrestrial Laser Scanning and Particle Tracking Velocimetry for Characterization of Landslide Model Failure J. Ma et al. 10.3390/s20010219
70. Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran V. Nhu et al. 10.3390/f11040421
71. Future changes in precipitation-caused landslide frequency in British Columbia S. Sobie 10.1007/s10584-020-02788-1
72. An assessment of the spatial distribution of shallow landslides induced by Hurricane María in Puerto Rico C. Ramos-Scharrón et al. 10.1080/02723646.2020.1801121
73. Assessment of stability of a Himalayan road cut slope with varying degrees of weathering: A finite-element-model-based approach G. Komadja et al. 10.1016/j.heliyon.2020.e05297
74. Indicator-based approach for assigning physical vulnerability of the houses to landslide hazard in the Himalayan region of India S. Bera et al. 10.1016/j.ijdrr.2020.101891
75. Potential of Documentary Evidence to Study Fatalities of Hydrological and Meteorological Events in the Czech Republic . Brázdil et al. 10.3390/w11102014
76. Shape and Dimension Estimations of Landslide Rupture Zones via Correlations of Characteristic Parameters G. Domej et al. 10.3390/geosciences10050198
77. Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis L. Hao et al. 10.5194/essd-12-2899-2020
78. Forecasting landslides using SIGMA model: a case study from Idukki, India M. Abraham et al. 10.1080/19475705.2021.1884610
79. Incorporation of Satellite Precipitation Uncertainty in a Landslide Hazard Nowcasting System S. Hartke et al. 10.1175/JHM-D-19-0295.1
80. Constraining landslide timing in a data-scarce context: from recent to very old processes in the tropical environment of the North Tanganyika-Kivu Rift region O. Dewitte et al. 10.1007/s10346-020-01452-0
81. GIS-Based Expert Knowledge for Landslide Susceptibility Mapping (LSM): Case of Mostaganem Coast District, West of Algeria R. Senouci et al. 10.3390/su13020630
82. Evaluation of social vulnerability to natural hazards: a case of Barton on Sea, England K. Kantamaneni 10.1007/s12517-019-4819-9
83. Building a landslide hazard indicator with machine learning and land surface models T. Stanley et al. 10.1016/j.envsoft.2020.104692
84. Physically-based catchment-scale prediction of slope failure volume and geometry B. Van den Bout et al. 10.1016/j.enggeo.2020.105942
85. Landslides across the USA: occurrence, susceptibility, and data limitations B. Mirus et al. 10.1007/s10346-020-01424-4
86. Displacement mechanisms of slow-moving landslides in response to changes in porewater pressure and dynamic stress J. Carey et al. 10.5194/esurf-7-707-2019
87. Global detection of rainfall-triggered landslide clusters S. Benz & P. Blum 10.5194/nhess-19-1433-2019
88. Mapping method of rainfall-induced landslide hazards by infiltration and slope stability analysis N. Dolojan et al. 10.1007/s10346-020-01617-x
89. Hong Kong’s landslip warning system—40 years of progress V. Kong et al. 10.1007/s10346-020-01379-6
90. Integrating principal component analysis with statistically-based models for analysis of causal factors and landslide susceptibility mapping: A comparative study from the loess plateau area in Shanxi (China) Y. Tang et al. 10.1016/j.jclepro.2020.124159
91. Twelve‐Year Dynamics and Rainfall Thresholds for Alternating Creep and Rapid Movement of the Hooskanaden Landslide From Integrating InSAR, Pixel Offset Tracking, and Borehole and Hydrological Measurements Y. Xu et al. 10.1029/2020JF005640
92. Fast physically-based model for rainfall-induced landslide susceptibility assessment at regional scale V. Medina et al. 10.1016/j.catena.2021.105213
93. Human Vulnerability to Landslides W. Pollock & J. Wartman 10.1029/2020GH000287
94. Internal Force Analysis and Field Test of Lattice Beam Based on Winkler Theory for Elastic Foundation Beam J. Li et al. 10.1155/2019/5130654
95. A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm D. Sun et al. 10.1016/j.geomorph.2020.107201
96. Rainfall triggers more deep-seated landslides than Cascadia earthquakes in the Oregon Coast Range, USA S. LaHusen et al. 10.1126/sciadv.aba6790
97. Invited perspectives: Building sustainable and resilient communities – recommended actions for natural hazard scientists J. Gill et al. 10.5194/nhess-21-187-2021
98. Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting A. van Natijne et al. 10.3390/s20051425
99. Historical Landslide Fatalities in British Columbia, Canada: Trends and Implications for Risk Management A. Strouth & S. McDougall 10.3389/feart.2021.606854
100. Relationships between earthquakes, hurricanes, and landslides in Costa Rica A. Quesada-Román et al. 10.1007/s10346-019-01209-4
101. Determination of the Probabilities of Landslide Events—A Case Study of Bhutan R. Sarkar & K. Dorji 10.3390/hydrology6020052
102. A Landslide Susceptibility Assessment Method Based on GIS Technology and an AHP-Weighted Information Content Method: A Case Study of Southern Anhui, China H. He et al. 10.3390/ijgi8060266
103. A comprehensive global database of giant landslides on volcanic islands J. Blahůt et al. 10.1007/s10346-019-01275-8
104. Numerical investigation on the sliding process and deposit feature of an earthquake-induced landslide: a case study H. Wang et al. 10.1007/s10346-020-01446-y
105. Spatial and temporal patterns and the socioeconomic impacts of landslides in the tropical and mountainous Colombian Andes E. Aristizábal & O. Sánchez 10.1111/disa.12391
106. Enhancing the reliability of landslide early warning systems by machine learning H. Thirugnanam et al. 10.1007/s10346-020-01453-z
107. Landslide Hazard and Exposure Modelling in Data‐Poor Regions: The Example of the Rohingya Refugee Camps in Bangladesh R. Emberson et al. 10.1029/2020EF001666
108. Simple rules to minimise exposure to coseismic landslide hazard D. Milledge et al. 10.5194/nhess-19-837-2019
109. Temporal Probability Assessment and Its Use in Landslide Susceptibility Mapping for Eastern Bhutan A. Dikshit et al. 10.3390/w12010267
110. Using a Tank Model to Determine Hydro-Meteorological Thresholds for Large-Scale Landslides in Taiwan G. Lin et al. 10.3390/w12010253
111. A national rockfall dataset as a tool for analysing the spatial and temporal rockfall occurrence in Germany S. Rupp & B. Damm 10.1002/esp.4827
112. GIS-Based Landslide Susceptibility Mapping for Land Use Planning and Risk Assessment A. Roccati et al. 10.3390/land10020162
113. Including informal housing in slope stability analysis – an application to a data-scarce location in the humid tropics E. Bozzolan et al. 10.5194/nhess-20-3161-2020
114. Geographical landslide early warning systems F. Guzzetti et al. 10.1016/j.earscirev.2019.102973
115. Resettlement preferences from landslide prone areas in Cameroon: Willingness to move, reasons to stay M. Baert et al. 10.1016/j.landusepol.2019.04.036
116. Thermal Remote Sensing from UAVs: A Review on Methods in Coastal Cliffs Prone to Landslides M. Melis et al. 10.3390/rs12121971
117. Estimates of three-dimensional rupture surface geometry of deep-seated landslides using landslide inventories and high-resolution topographic data M. Bunn et al. 10.1016/j.geomorph.2020.107332
118. Automatic calculation of rainfall thresholds for landslide occurrence in Chukha Dzongkhag, Bhutan S. Gariano et al. 10.1007/s10064-018-1415-2
119. Invited perspectives: Mountain roads in Nepal at a new crossroads K. Sudmeier-Rieux et al. 10.5194/nhess-19-655-2019
120. Three-dimensional rockfall shape back analysis: methods and implications D. Bonneau et al. 10.5194/nhess-19-2745-2019
121. A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units S. Zhao et al. 10.1155/2021/8854606
122. The State of Remote Sensing Capabilities of Cascading Hazards Over High Mountain Asia D. Kirschbaum et al. 10.3389/feart.2019.00197
123. Topographic uncertainty quantification for flow-like landslide models via stochastic simulations H. Zhao & J. Kowalski 10.5194/nhess-20-1441-2020
124. Towards establishing rainfall thresholds for a real-time landslide early warning system in Sikkim, India G. Harilal et al. 10.1007/s10346-019-01244-1
125. Spatio-temporal cluster analysis of recent Italian landslides M. Tonini et al. 10.1080/17499518.2020.1861634
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128. Landslide mapping with remote sensing: challenges and opportunities C. Zhong et al. 10.1080/01431161.2019.1672904
129. Temporal and spatial distributions of landslides in the Qinba Mountains, Shaanxi Province, China H. Qiu et al. 10.1080/19475705.2018.1536080
130. Vulnerability index related to populations at-risk for landslides in the Brazilian Early Warning System (BEWS) M. Assis Dias et al. 10.1016/j.ijdrr.2020.101742
131. Landslide Prediction Method Based on a Ground-Based Micro-Deformation Monitoring Radar L. Qi et al. 10.3390/rs12081230
132. Widespread Initiation, Reactivation, and Acceleration of Landslides in the Northern California Coast Ranges due to Extreme Rainfall A. Handwerger et al. 10.1029/2019JF005035
133. Nationwide Susceptibility Mapping of Landslides in Kenya Using the Fuzzy Analytic Hierarchy Process Model S. Zhou et al. 10.3390/land9120535
134. A multistage hybrid model for landslide risk mapping: tested in and around Mussoorie in Uttarakhand state of India M. Mishra & T. Sarkar 10.1007/s12665-020-09180-3
135. Preface to the Special Issue “Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning” S. Segoni et al. 10.3390/w13030323
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137. Sendai voluntary commitments: landslide stakeholders and the all-of-society approach enhanced by UNDRR Y. Matsuoka & E. Gonzales Rocha 10.1007/s10346-020-01519-y
138. A GIS-based factor clustering and landslide susceptibility analysis using AHP for Gish River Basin, India T. Basu & S. Pal 10.1007/s10668-019-00406-4
139. Causes and triggers of deep-seated hillslope instability in the tropics – Insights from a 60-year record of Ikoma landslide (DR Congo) A. Dille et al. 10.1016/j.geomorph.2019.106835
140. A Modeling Platform for Landslide Stability: A Hydrological Approach . Emadi-Tafti & . Ataie-Ashtiani 10.3390/w11102146
141. INSPIRE standards as a framework for artificial intelligence applications: a landslide example G. Roberti et al. 10.5194/nhess-20-3455-2020
142. Performance evaluation for four GIS-based models purposed to predict and map landslide susceptibility: A case study at a World Heritage site in Southwest China Y. Jiao et al. 10.1016/j.catena.2019.104221
143. Rainfall Threshold Estimation and Landslide Forecasting for Kalimpong, India Using SIGMA Model M. Abraham et al. 10.3390/w12041195
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145. Topographic Analysis of Landslide Distribution Using AW3D30 Data A. Nonomura et al. 10.3390/geosciences10040115
146. Stability analysis of open-pit gold mine slopes and optimization of mining scheme in Inner Mongolia, China S. Ren et al. 10.1007/s11629-020-6217-x
147. Forecasting Landslides Using Mobility Functions: A Case Study from Idukki District, India M. Abraham et al. 10.1007/s40098-020-00490-8
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149. Granular flow simulation in a centrifugal acceleration field M. Cabrera et al. 10.1680/jgeot.18.P.260
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151. Definition of 3D rainfall thresholds to increase operative landslide early warning system performances A. Rosi et al. 10.1007/s10346-020-01523-2
152. Engineering geomorphological investigation of the Kasavu landslide, Viti Levu, Fiji A. Ram et al. 10.1007/s10346-019-01191-x
153. Dynamics of pore water pressure at the soil–bedrock interface recorded during a rainfall-induced shallow landslide in a steep natural forested headwater catchment, Taiwan W. Liang 10.1016/j.jhydrol.2020.125003
154. Review article: Natural hazard risk assessments at the global scale P. Ward et al. 10.5194/nhess-20-1069-2020
155. Geomorphology of landslide–flood-critical areas in hilly catchments and urban areas for EWS (Feltrino Stream and Lanciano town, Abruzzo, Central Italy) C. Carabella et al. 10.1080/17445647.2020.1819903
156. Study on the stability of brown coal batter with opened cracks on Maddingley brown coal mine L. Zhao & G. You 10.1007/s42452-020-2897-7
157. Landslide susceptibility mapping with r.landslide: A free open-source GIS-integrated tool based on Artificial Neural Networks L. Bragagnolo et al. 10.1016/j.envsoft.2019.104565
158. Dry Spells and Extreme Precipitation are The Main Trigger of Landslides in Central Europe R. Tichavský et al. 10.1038/s41598-019-51148-2
159. Landslide monitoring using seismic ambient noise correlation: challenges and applications M. Le Breton et al. 10.1016/j.earscirev.2021.103518
160. Geomorphological hazards susceptibility in high-density urban areas: A case study of Mexico City D. García-Soriano et al. 10.1016/j.jsames.2020.102667
161. Modeling impacts of climate change on return period of landslide triggering D. Peres & A. Cancelliere 10.1016/j.jhydrol.2018.10.036
162. Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models F. Huang et al. 10.3390/ijgi9060377
163. Co-seismic landslide mapping using Sentinel-2 10-m fused NIR narrow, red-edge, and SWIR bands P. Lu et al. 10.1007/s10346-021-01636-2
164. Forecasting the time of failure of landslides at slope-scale: A literature review E. Intrieri et al. 10.1016/j.earscirev.2019.03.019
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166. Spatial dendrogeomorphic sampling based on the specific tree growth responses induced by the landslide mechanism K. Šilhán 10.1016/j.quageo.2020.101132
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175. Post-failure landslide change detection and analysis using optical satellite Sentinel-2 images F. Qu et al. 10.1007/s10346-020-01498-0
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