138 citations as recorded by crossref.

1. Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data C. Eiras et al. 10.1007/s11069-021-04638-4
2. Mapping landslide susceptibility using data-driven methods J. Zêzere et al. 10.1016/j.scitotenv.2017.02.188
3. Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy) M. Cama et al. 10.5194/nhess-15-1785-2015
4. The effect of different mobile uses on crash frequency among young drivers: application of statistical models and clustering analysis U. Gazder et al. 10.1080/17457300.2022.2092872
5. Application and comparison of different ensemble learning machines combining with a novel sampling strategy for shallow landslide susceptibility mapping Z. Liang et al. 10.1007/s00477-020-01893-y
6. In Pursuit of Inclusion: Studying the Impact of EDI Policies in the Workplace and Exploring the Challenges of Measurement T. Garakani et al. 10.7202/1111225ar
7. Landslide Susceptibility Mapping Based on Deep Learning Algorithms Using Information Value Analysis Optimization J. Ji et al. 10.3390/land12061125
8. Pixel-scale gully erosion susceptibility: Predictive modeling with R using gully inventory consistent with terrain variables C. Conoscenti et al. 10.1016/j.catena.2025.109091
9. Identification of urban sectors prone to solid waste accumulation: A machine learning approach based on social indicators L. Izquierdo-Horna et al. 10.1016/j.compenvurbsys.2022.101834
10. Mapping the ratoon rice suitability region in China using random forest and recursive feature elimination modeling Y. Wang & Y. Li 10.1016/j.fcr.2023.109016
11. Areas simultaneously susceptible and (dis-)connected to debris flows in the Dolomites (Italy): regional-scale application of a novel data-driven approach F. Pitscheider et al. 10.1080/17445647.2024.2307549
12. Field-based landslide susceptibility assessment in a data-scarce environment: the populated areas of the Rwenzori Mountains L. Jacobs et al. 10.5194/nhess-18-105-2018
13. A comparative analysis of statistical landslide susceptibility mapping in the southeast region of Minas Gerais state, Brazil C. Barella et al. 10.1007/s10064-018-1341-3
14. Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility P. Lima et al. 10.1007/s11629-021-7254-9
15. Evaluation of road blockage induced by seismic landslides under 2021 MS6.4 Yangbi earthquake Y. Wu et al. 10.1007/s12665-023-11319-x
16. Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps S. Steger et al. 10.1016/j.geomorph.2016.03.015
17. Machine learning-based thermokarst landslide susceptibility modeling across the permafrost region on the Qinghai-Tibet Plateau G. Yin et al. 10.1007/s10346-021-01669-7
18. A modified Logit model for assessment and validation of debris-flow susceptibility S. Wu et al. 10.1007/s10064-018-1412-5
19. Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis L. Yang et al. 10.3390/w16070923
20. Analysing the Large-Scale Debris Flow Event in July 2022 in Horlachtal, Austria Using Remote Sensing and Measurement Data J. Rom et al. 10.3390/geosciences13040100
21. Presenting logistic regression-based landslide susceptibility results L. Lombardo & P. Mai 10.1016/j.enggeo.2018.07.019
22. A novel method for estimating missing values in ship principal data Y. Kim et al. 10.1016/j.oceaneng.2022.110979
23. Spatio-temporal analysis of slope-type debris flow activity in Horlachtal, Austria, based on orthophotos and lidar data since 1947 J. Rom et al. 10.5194/nhess-23-601-2023
24. Emulation of 2D Hydrodynamic Flood Simulations at Catchment Scale Using ANN and SVR S. Mirza Alipour & J. Leal 10.3390/w13202858
25. Presence-only approach to assess landslide triggering-thickness susceptibility: a test for the Mili catchment (north-eastern Sicily, Italy) L. Lombardo et al. 10.1007/s11069-016-2443-5
26. Comparative Performance of Machine Learning Models for Landslide Susceptibility Assessment: Impact of Sampling Strategies in Highway Buffer Zone Z. Tang et al. 10.3390/app15158416
27. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model H. Hussin et al. 10.1016/j.geomorph.2015.10.030
28. Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster L. Lombardo et al. 10.1007/s00477-018-1518-0
29. The influence of sampling on landslide susceptibility mapping using artificial neural networks S. Gameiro et al. 10.1080/10106049.2022.2144475
30. Spatial patterns and drivers of smallholder oil palm expansion within peat swamp forests of Riau, Indonesia J. Zhao et al. 10.1088/1748-9326/ac4dc6
31. Landslide susceptibility mapping of the Sera River Basin using logistic regression model N. Raja et al. 10.1007/s11069-016-2591-7
32. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling J. Goetz et al. 10.1016/j.cageo.2015.04.007
33. A Preliminary Study of Prescription for Play on Developmental Concerns M. Oo et al. 10.1177/00099228241258846
34. Optimizing and validating the Gravitational Process Path model for regional debris-flow runout modelling J. Goetz et al. 10.5194/nhess-21-2543-2021
35. Evaluation of landslide susceptibility based on SMOTE-Tomek sampling and machine learning algorithm M. Lv et al. 10.1371/journal.pone.0323487
36. The propagation of inventory-based positional errors into statistical landslide susceptibility models S. Steger et al. 10.5194/nhess-16-2729-2016
37. Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: A comparison between logistic regression and random forest D. Sun et al. 10.1016/j.enggeo.2020.105972
38. Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling J. Lai et al. 10.3390/ijgi8090397
39. The influence of cartographic representation on landslide susceptibility models: empirical evidence from a Brazilian UNESCO world heritage site J. Araujo Junior et al. 10.1007/s11069-024-06576-3
40. Artificial neural networks applied to landslide susceptibility: The effect of sampling areas on model capacity for generalization and extrapolation S. Gameiro et al. 10.1016/j.apgeog.2021.102598
41. Sediment Yield in Dam-Controlled Watersheds in the Pisha Sandstone Region on the Northern Loess Plateau, China F. Xie et al. 10.3390/land10111264
42. Application of novel ensemble models to improve landslide susceptibility mapping reliability Z. Tong et al. 10.1007/s10064-023-03328-8
43. Development and validation of risk prediction model for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, 2005–2021 N. Gelaw et al. 10.1371/journal.pone.0276472
44. Predicting sediment deposition rate in check-dams using machine learning techniques and high-resolution DEMs C. Conoscenti et al. 10.1007/s12665-021-09695-3
45. Improved Estimation of Aboveground Biomass of Disturbed Grassland through Including Bare Ground and Grazing Intensity Y. Shi et al. 10.3390/rs13112105
46. The dilemma of determining the superiority of data mining models: optimal sampling balance and end users’ perspectives matter M. Teimouri & A. Kornejady 10.1007/s10064-019-01687-9
47. Predicting gully occurrence at watershed scale: Comparing topographic indices and multivariate statistical models C. Conoscenti & E. Rotigliano 10.1016/j.geomorph.2020.107123
48. Landslide susceptibility mapping using an integration of different statistical models for the 2015 Nepal earthquake in Tibet S. Huang & L. Chen 10.1080/19475705.2024.2396908
49. The influence of spatial patterns in rainfall on shallow landslides H. Smith et al. 10.1016/j.geomorph.2023.108795
50. Assessment of Thaw-Hazard susceptibility in permafrost regions using multisource data and machine learning: A case study in Qinghai Province, China G. Wei et al. 10.1016/j.coldregions.2025.104648
51. A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020 J. Huang et al. 10.1007/s11356-022-23732-z
52. Investigating the effects of landslides inventory completeness on susceptibility mapping and frequency-area distributions: Case of Taounate province, Northern Morocco R. Sahrane et al. 10.1016/j.catena.2022.106737
53. Intersection of fluvial incision and weak geologic structures cause divergence from a universal threshold slope model of landslide occurrence F. Williams et al. 10.1016/j.geomorph.2021.107795
54. Terrain Controls and Landscape-Scale Susceptibility Modelling of Active-Layer Detachments, Sabine Peninsula, Melville Island, Nunavut A. Rudy et al. 10.1002/ppp.1900
55. The Effect of Observation Data Sampling Methods on Infiltration Areas by Maximum Entropy Model M. Jafarzadeh et al. 10.52547/jwmr.11.22.96
56. The performance of using an autoencoder for prediction and susceptibility assessment of landslides: A case study on landslides triggered by the 2018 Hokkaido Eastern Iburi earthquake in Japan K. Nam & F. Wang 10.1186/s40677-019-0137-5
57. Forest Harvest Patterns on Private Lands in the Cascade Mountains, Washington, USA C. Soulard et al. 10.3390/f8100383
58. GIS-based Groundwater Spring Potential Mapping Using Data Mining Boosted Regression Tree and Probabilistic Frequency Ratio Models in Iran S. Mohsen Mousavi et al. 10.3934/geosci.2017.1.91
59. A systematic approach and software for the analysis of point patterns on river networks W. Schwanghart et al. 10.1002/esp.5127
60. A comparison of raster-based travel time surfaces against vector-based network calculations as applied in the study of rural food deserts T. Mulrooney et al. 10.1016/j.apgeog.2016.10.006
61. The influence of forest cover on landslide occurrence explored with spatio-temporal information E. Schmaltz et al. 10.1016/j.geomorph.2017.04.024
62. Comparing methods of landslide data acquisition and susceptibility modelling: Examples from New Zealand H. Smith et al. 10.1016/j.geomorph.2021.107660
63. Rheological characterization of debris flows in the western himalayas using XGBoost and laboratory data N. Pandey & N. Satyam 10.1007/s40808-025-02311-4
64. Transferability of regional permafrost disturbance susceptibility modelling using generalized linear and generalized additive models A. Rudy et al. 10.1016/j.geomorph.2016.04.011
65. Landslide susceptibility assessment in Qinzhou based on rough set and semi-supervised support vector machine C. Kong et al. 10.1007/s12145-023-01087-4
66. Dynamic landslide susceptibility mapping based on the PS-InSAR deformation intensity B. Jin et al. 10.1007/s11356-023-31688-x
67. Influence of sampling design on landslide susceptibility modeling in lithologically heterogeneous areas A. Dornik et al. 10.1038/s41598-022-06257-w
68. Comparing physical and statistical landslide susceptibility models at the scale of individual trees R. Spiekermann et al. 10.1016/j.geomorph.2023.108870
69. Bahaya Longsor di Kabupaten Sukabumi berbasis Metode Weight of Evidence (WoE), Logistic Regression (LR) dan Kombinasi WoE-LR S. Kusmajaya et al. 10.29244/jitl.22.2.101-106
70. Exploring the effect of absence selection on landslide susceptibility models: A case study in Sicily, Italy C. Conoscenti et al. 10.1016/j.geomorph.2016.03.006
71. LAND-SE: a software for statistically based landslide susceptibility zonation, version 1.0 M. Rossi & P. Reichenbach 10.5194/gmd-9-3533-2016
72. A statistical approach for sea cliff hazard and setback lines mapping in the northeast of Peniche, Portugal N. Penacho & F. Marques 10.1016/j.geomorph.2023.108669
73. Analysis of land use/land cover change (LULCC) and debris flow risks in Adama district, Ethiopia, aided by numerical simulation and deep learning-based remote sensing A. Bojer et al. 10.1007/s00477-023-02550-w
74. Assessing the Reliability of Landslides Susceptibility Models with Limited Data: Impact of Geomorphological Diversity and Technique Selection on Model Performance in Taounate Province, Northern Morocco R. Sahrane et al. 10.1007/s41748-024-00455-4
75. Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size P. Tsangaratos & I. Ilia 10.1016/j.catena.2016.06.004
76. LANDSLIDE SUSCEPTIBILITY ASSESSMENT USING GEOGRAPHICAL INFORMATION SYSTEM BASED LOGISTIC REGRESSION SYSTEM BETWEEN ÇUBUK-KALECİK (ANKARA) AND ŞABANÖZÜ (ÇANKIRI) REGIONS H. ELMACI et al. 10.19111/bulletinofmre.306692
77. Regional variability of terrain index and machine learning model applications for prediction of ephemeral gullies C. Conoscenti & A. Sheshukov 10.1016/j.geomorph.2023.108915
78. Predicting the landslides triggered by the 2009 96E/Ida tropical storms in the Ilopango caldera area (El Salvador, CA): optimizing MARS-based model building and validation strategies E. Rotigliano et al. 10.1007/s12665-019-8214-3
79. Machine learning for landslides prevention: a survey Z. Ma et al. 10.1007/s00521-020-05529-8
80. Semi-quantitative method to identify the vulnerable areas in terms of building aggregation for probable landslide runout at the regional scale: a case study from Soacha Province, Colombia A. Pradhan et al. 10.1007/s10064-019-01533-y
81. Investigating the Effects of Landslides Inventory Completeness on Susceptibility Mapping and Frequency-Area Distributions: Case of Taounate Province, Northern Morocco R. Sahrane et al. 10.2139/ssrn.4144329
82. Susceptibility assessment of environmental geological disasters in Liulin County based on RF: from the perspective of positive and negative sample proportion Z. Wang et al. 10.1007/s11356-023-30778-0
83. Improving GIS-Based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning J. Lai & F. Tsai 10.3390/s19173717
84. A debate on the relationship between out-of-hospital cardiac arrest attributed to poisoning and good neurological outcome C. Zhou et al. 10.1016/j.resuscitation.2022.03.031
85. A Combined Weight of Evidence and Logistic Regression Method for Susceptibility Mapping of Earthquake‐induced Landslides: A Case Study of the April 20, 2013 Lushan Earthquake, China Z. Suhua et al. 10.1111/1755-6724.12687
86. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy) A. Trigila et al. 10.1016/j.geomorph.2015.06.001
87. Improvement of the predictive performance of landslide mapping models in mountainous terrains using cluster sampling M. Riaz et al. 10.1080/10106049.2022.2066202
88. Brief communication: Visualizing uncertainties in landslide susceptibility modelling using bivariate mapping M. Schlögl et al. 10.5194/nhess-25-1425-2025
89. Dependence of debris flow susceptibility maps on sampling strategy with data-driven grid-based model N. Jiang et al. 10.1016/j.ecolind.2024.112534
90. An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping: Application in Two Areas of Three Gorges Reservoir, China D. Sun et al. 10.1007/s12583-020-1072-9
91. An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture, Japan K. Nam & F. Wang 10.1186/s40677-020-0143-7
92. Improving transferability strategies for debris flow susceptibility assessment: Application to the Saponara and Itala catchments (Messina, Italy) M. Cama et al. 10.1016/j.geomorph.2017.03.025
93. Susceptibility Assessment for Landslide Initiated along Power Transmission Lines S. Liu et al. 10.3390/rs13245068
94. Hybrid-optimized logistic regression model of landslide susceptibility along mountain highway Y. Zhang et al. 10.1007/s10064-021-02415-y
95. Predicting Earthquake-Induced Landslides by Using a Stochastic Modeling Approach: A Case Study of the 2001 El Salvador Coseismic Landslides C. Mercurio et al. 10.3390/ijgi12040178
96. Enhancing Flood Risk Assessment Using Multisensor Remote Sensing Data and Hydraulic Modeling Q. Wang et al. 10.1109/JSTARS.2025.3589949
97. Effects of physical activity and use of digital devices on visual acuity in children and adolescents during the COVID-19 pandemic: A cross-sectional study X. Zheng et al. 10.3389/fpubh.2022.1017479
98. Challenges of DHS and MIS to capture the entire pattern of malaria parasite risk and intervention effects in countries with different ecological zones: the case of Cameroon S. Massoda Tonye et al. 10.1186/s12936-018-2284-7
99. Predicting Landslide Deposit Zones: Insights from Advanced Sampling Strategies in the Ilopango Caldera, El Salvador L. Calderon-Cucunuba et al. 10.3390/land14020269
100. The influence of systematically incomplete shallow landslide inventories on statistical susceptibility models and suggestions for improvements S. Steger et al. 10.1007/s10346-017-0820-0
101. Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey M. Ada & B. San 10.1007/s11069-017-3043-8
102. Data-driven analysis of shallow landslide-to-stream connectivity A. Tsyplenkov et al. 10.1016/j.gespch.2025.100002
103. Using Machine Learning to Predict Geomorphic Disturbance: The Effects of Sample Size, Sample Prevalence, and Sampling Strategy G. Perry & M. Dickson 10.1029/2018JF004640
104. 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. 10.1016/j.geomorph.2023.108638
105. Sample size effects on landslide susceptibility models: A comparative study of heuristic, statistical, machine learning, deep learning and ensemble learning models with SHAP analysis S. Yang et al. 10.1016/j.cageo.2024.105723
106. Risk assessment of landslide and rockfall hazards in hilly region of southwestern China: a case study of Qijiang, Wuxi and Chishui P. Ye et al. 10.1007/s12665-024-11698-9
107. Completeness of landslide inventory and landslide susceptibility mapping using logistic regression method in Ceyhan Watershed (southern Turkey) S. Tekin 10.1007/s12517-021-07583-5
108. Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) B. Kalantar et al. 10.1080/19475705.2017.1407368
109. Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China Y. Wang et al. 10.3390/ijerph16030368
110. Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model D. Camilo et al. 10.1016/j.envsoft.2017.08.003
111. Performance of the LandSoil expert‐based model to map erosion and sedimentation: application to a cultivated catchment in central Belgium V. Cantreul et al. 10.1002/esp.4808
112. Modelling golden eagle habitat selection and flight activity in their home ranges for safer wind farm planning H. Tikkanen et al. 10.1016/j.eiar.2018.04.006
113. The strength of competition and market efficiency in determining bank profits S. Suroso & C. Mala 10.22495/rgcv14i3p1
114. A prediction model for Xiangyang Neolithic sites based on a random forest algorithm L. Li et al. 10.1515/geo-2022-0467
115. Expanding cryospheric landform inventories – quantitative approaches for underestimated periglacial block- and talus slopes in the Dry Andes of Argentina T. Köhler et al. 10.3389/feart.2025.1534410
116. Exploring the Application of a Debris Flow Likelihood Regression Model in Mediterranean Post-Fire Environments, Using Field Observations-Based Validation M. Diakakis et al. 10.3390/land12030555
117. A hybrid machine-learning model to map glacier-related debris flow susceptibility along Gyirong Zangbo watershed under the changing climate C. Qiu et al. 10.1016/j.scitotenv.2021.151752
118. Quantifying effectiveness of trees for landslide erosion control R. Spiekermann et al. 10.1016/j.geomorph.2021.107993
119. The impact and interactive effects of multi-level spatial policies on urban renewal: A case study of Shenzhen, China K. Cao & Y. Deng 10.1016/j.habitatint.2023.102952
120. Projected Rainfall Triggered Landslide Susceptibility Changes in the Hengduan Mountain Region, Southwest China under 1.5–4.0 °C Warming Scenarios Based on CMIP6 Models H. Yin et al. 10.3390/atmos14020214
121. Data‐driven modelling of joint debris flow release susceptibility and connectivity S. Steger et al. 10.1002/esp.5421
122. A benchmark dataset and workflow for landslide susceptibility zonation M. Alvioli et al. 10.1016/j.earscirev.2024.104927
123. Landslide susceptibility: a statistically-based assessment on a depositional pyroclastic ramp F. Murillo-García et al. 10.1007/s11629-018-5225-6
124. Evaluating map quality and model performance through iterative statistics-based landslide susceptibility in eastern KY M. Crawford et al. 10.1007/s11069-025-07255-7
125. Prediction of debris-avalanches and -flows triggered by a tropical storm by using a stochastic approach: An application to the events occurred in Mocoa (Colombia) on 1 April 2017 G. Vargas-Cuervo et al. 10.1016/j.geomorph.2019.04.023
126. Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation D. Sun et al. 10.3390/land12051018
127. Multivariate geostatistical modeling of the spatial sediment distribution in a large scale drainage basin, Upper Rhone, Switzerland A. Schoch et al. 10.1016/j.geomorph.2017.11.026
128. GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models W. Chen et al. 10.1080/19475705.2017.1289250
129. When Enough Is Really Enough? On the Minimum Number of Landslides to Build Reliable Susceptibility Models G. Titti et al. 10.3390/geosciences11110469
130. Role of Finer Particles in Rheological Characterization of Debris Flows: Insights from Western Himalayas N. Pandey & N. Satyam 10.1007/s40098-025-01286-4
131. COĞRAFİ BİLGİ SİSTEMİ TABANLI MANTIKSAL REGRESYON YÖNTEMİ KULLANILARAK ÇUBUK-KALECİK (ANKARA) ŞABANÖZÜ (ÇANKIRI) ARASININ HEYELAN DUYARLILIK DEĞERLENDİRMESİ H. ELMACI et al. 10.19076/mta.304329
132. Combining spatial modelling and regionalization of rainfall thresholds for debris flows hazard mapping in the Emilia-Romagna Apennines (Italy) C. G. et al. 10.1007/s10346-021-01739-w
133. Multi-Temporal Analysis of Forest Fire Probability Using Socio-Economic and Environmental Variables S. Kim et al. 10.3390/rs11010086
134. Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment (Sicily, Italy) M. Cama et al. 10.1007/s12665-015-5047-6
135. National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data Q. Lin et al. 10.1016/j.gsf.2021.101248
136. Improving the reliability of landslide susceptibility mapping through spatial uncertainty analysis: a case study of Al Hoceima, Northern Morocco H. Rahali 10.1080/10106049.2017.1357767
137. Assessing the quality of landslide susceptibility maps – case study Lower Austria H. Petschko et al. 10.5194/nhess-14-95-2014
138. Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France) M. Fressard et al. 10.5194/nhess-14-569-2014