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.
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4. The effect of different mobile uses on crash frequency among young drivers: application of statistical models and clustering analysis U. Gazder et al.
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6. In Pursuit of Inclusion: Studying the Impact of EDI Policies in the Workplace and Exploring the Challenges of Measurement T. Garakani et al.
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12. Field-based landslide susceptibility assessment in a data-scarce environment: the populated areas of the Rwenzori Mountains L. Jacobs et al.
13. A comparative analysis of statistical landslide susceptibility mapping in the southeast region of Minas Gerais state, Brazil C. Barella et al.
14. Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility P. Lima et al.
15. Evaluation of road blockage induced by seismic landslides under 2021 MS6.4 Yangbi earthquake Y. Wu et al.
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20. Analysing the Large-Scale Debris Flow Event in July 2022 in Horlachtal, Austria Using Remote Sensing and Measurement Data J. Rom et al.
21. Comparison of landslide susceptibility assessment in volcanic terrains based on a landform unit model and two multivariate logistic regression models G. Paulin et al.
22. Presenting logistic regression-based landslide susceptibility results L. Lombardo & P. Mai
23. A novel method for estimating missing values in ship principal data Y. Kim et al.
24. Spatio-temporal analysis of slope-type debris flow activity in Horlachtal, Austria, based on orthophotos and lidar data since 1947 J. Rom et al.
25. Emulation of 2D Hydrodynamic Flood Simulations at Catchment Scale Using ANN and SVR S. Mirza Alipour & J. Leal
26. Presence-only approach to assess landslide triggering-thickness susceptibility: a test for the Mili catchment (north-eastern Sicily, Italy) L. Lombardo et al.
27. Comparative Performance of Machine Learning Models for Landslide Susceptibility Assessment: Impact of Sampling Strategies in Highway Buffer Zone Z. Tang et al.
28. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model H. Hussin et al.
29. Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster L. Lombardo et al.
30. The influence of sampling on landslide susceptibility mapping using artificial neural networks S. Gameiro et al.
31. Spatial patterns and drivers of smallholder oil palm expansion within peat swamp forests of Riau, Indonesia J. Zhao et al.
32. Landslide susceptibility mapping of the Sera River Basin using logistic regression model N. Raja et al.
33. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling J. Goetz et al.
34. A Preliminary Study of Prescription for Play on Developmental Concerns M. Oo et al.
35. Optimizing and validating the Gravitational Process Path model for regional debris-flow runout modelling J. Goetz et al.
36. Evaluation of landslide susceptibility based on SMOTE-Tomek sampling and machine learning algorithm M. Lv et al.
37. The propagation of inventory-based positional errors into statistical landslide susceptibility models S. Steger et al.
38. Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: A comparison between logistic regression and random forest D. Sun et al.
39. Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling J. Lai et al.
40. The influence of cartographic representation on landslide susceptibility models: empirical evidence from a Brazilian UNESCO world heritage site J. Araujo Junior et al.
41. Artificial neural networks applied to landslide susceptibility: The effect of sampling areas on model capacity for generalization and extrapolation S. Gameiro et al.
42. Sediment Yield in Dam-Controlled Watersheds in the Pisha Sandstone Region on the Northern Loess Plateau, China F. Xie et al.
43. Application of novel ensemble models to improve landslide susceptibility mapping reliability Z. Tong et al.
44. 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.
45. Predicting sediment deposition rate in check-dams using machine learning techniques and high-resolution DEMs C. Conoscenti et al.
46. Improved Estimation of Aboveground Biomass of Disturbed Grassland through Including Bare Ground and Grazing Intensity Y. Shi et al.
47. The dilemma of determining the superiority of data mining models: optimal sampling balance and end users’ perspectives matter M. Teimouri & A. Kornejady
48. Predicting gully occurrence at watershed scale: Comparing topographic indices and multivariate statistical models C. Conoscenti & E. Rotigliano
49. Landslide susceptibility mapping using an integration of different statistical models for the 2015 Nepal earthquake in Tibet S. Huang & L. Chen
50. The influence of spatial patterns in rainfall on shallow landslides H. Smith et al.
51. 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.
52. A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020 J. Huang et al.
53. Investigating the effects of landslides inventory completeness on susceptibility mapping and frequency-area distributions: Case of Taounate province, Northern Morocco R. Sahrane et al.
54. Intersection of fluvial incision and weak geologic structures cause divergence from a universal threshold slope model of landslide occurrence F. Williams et al.
55. Terrain Controls and Landscape-Scale Susceptibility Modelling of Active-Layer Detachments, Sabine Peninsula, Melville Island, Nunavut A. Rudy et al.
56. The Effect of Observation Data Sampling Methods on Infiltration Areas by Maximum Entropy Model M. Jafarzadeh et al.
57. 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
58. Forest Harvest Patterns on Private Lands in the Cascade Mountains, Washington, USA C. Soulard et al.
59. GIS-based Groundwater Spring Potential Mapping Using Data Mining Boosted Regression Tree and Probabilistic Frequency Ratio Models in Iran S. Mohsen Mousavi et al.
60. A systematic approach and software for the analysis of point patterns on river networks W. Schwanghart et al.
61. 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.
62. The influence of forest cover on landslide occurrence explored with spatio-temporal information E. Schmaltz et al.
63. Comparing methods of landslide data acquisition and susceptibility modelling: Examples from New Zealand H. Smith et al.
64. Rheological characterization of debris flows in the western himalayas using XGBoost and laboratory data N. Pandey & N. Satyam
65. Transferability of regional permafrost disturbance susceptibility modelling using generalized linear and generalized additive models A. Rudy et al.
66. Landslide susceptibility assessment in Qinzhou based on rough set and semi-supervised support vector machine C. Kong et al.
67. Dynamic landslide susceptibility mapping based on the PS-InSAR deformation intensity B. Jin et al.
68. Influence of sampling design on landslide susceptibility modeling in lithologically heterogeneous areas A. Dornik et al.
69. Comparing physical and statistical landslide susceptibility models at the scale of individual trees R. Spiekermann et al.
70. Bahaya Longsor di Kabupaten Sukabumi berbasis Metode Weight of Evidence (WoE), Logistic Regression (LR) dan Kombinasi WoE-LR S. Kusmajaya et al.
71. Exploring the effect of absence selection on landslide susceptibility models: A case study in Sicily, Italy C. Conoscenti et al.
72. LAND-SE: a software for statistically based landslide susceptibility zonation, version 1.0 M. Rossi & P. Reichenbach
73. A statistical approach for sea cliff hazard and setback lines mapping in the northeast of Peniche, Portugal N. Penacho & F. Marques
74. 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.
75. 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.
76. 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
77. LANDSLIDE SUSCEPTIBILITY ASSESSMENT USING GEOGRAPHICAL INFORMATION SYSTEM BASED LOGISTIC REGRESSION SYSTEM BETWEEN ÇUBUK-KALECİK (ANKARA) AND ŞABANÖZÜ (ÇANKIRI) REGIONS H. ELMACI et al.
78. Regional variability of terrain index and machine learning model applications for prediction of ephemeral gullies C. Conoscenti & A. Sheshukov
79. 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.
80. Validation and prediction challenges in data-driven landslide susceptibility mapping: insights from random sampling of training and test data in machine learning models C. Barella et al.
81. Machine learning for landslides prevention: a survey Z. Ma et al.
82. 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.
83. Investigating the Effects of Landslides Inventory Completeness on Susceptibility Mapping and Frequency-Area Distributions: Case of Taounate Province, Northern Morocco R. Sahrane et al.
84. 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.
85. Improving GIS-Based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning J. Lai & F. Tsai
86. A debate on the relationship between out-of-hospital cardiac arrest attributed to poisoning and good neurological outcome C. Zhou et al.
87. 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.
88. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy) A. Trigila et al.
89. Improvement of the predictive performance of landslide mapping models in mountainous terrains using cluster sampling M. Riaz et al.
90. Brief communication: Visualizing uncertainties in landslide susceptibility modelling using bivariate mapping M. Schlögl et al.
91. Dependence of debris flow susceptibility maps on sampling strategy with data-driven grid-based model N. Jiang et al.
92. 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.
93. An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture, Japan K. Nam & F. Wang
94. Improving transferability strategies for debris flow susceptibility assessment: Application to the Saponara and Itala catchments (Messina, Italy) M. Cama et al.
95. Susceptibility Assessment for Landslide Initiated along Power Transmission Lines S. Liu et al.
96. Hybrid-optimized logistic regression model of landslide susceptibility along mountain highway Y. Zhang et al.
97. Predicting Earthquake-Induced Landslides by Using a Stochastic Modeling Approach: A Case Study of the 2001 El Salvador Coseismic Landslides C. Mercurio et al.
98. Enhancing Flood Risk Assessment Using Multisensor Remote Sensing Data and Hydraulic Modeling Q. Wang et al.
99. 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.
100. 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.
101. Predicting Landslide Deposit Zones: Insights from Advanced Sampling Strategies in the Ilopango Caldera, El Salvador L. Calderon-Cucunuba et al.
102. The influence of systematically incomplete shallow landslide inventories on statistical susceptibility models and suggestions for improvements S. Steger et al.
103. 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
104. Data-driven analysis of shallow landslide-to-stream connectivity A. Tsyplenkov et al.
105. Using Machine Learning to Predict Geomorphic Disturbance: The Effects of Sample Size, Sample Prevalence, and Sampling Strategy G. Perry & M. Dickson
106. 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.
107. 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.
108. 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.
109. Completeness of landslide inventory and landslide susceptibility mapping using logistic regression method in Ceyhan Watershed (southern Turkey) S. Tekin
110. 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.
111. 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.
112. Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model D. Camilo et al.
113. Performance of the LandSoil expert‐based model to map erosion and sedimentation: application to a cultivated catchment in central Belgium V. Cantreul et al.
114. Modelling golden eagle habitat selection and flight activity in their home ranges for safer wind farm planning H. Tikkanen et al.
115. The strength of competition and market efficiency in determining bank profits S. Suroso & C. Mala
116. A prediction model for Xiangyang Neolithic sites based on a random forest algorithm L. Li et al.
117. Expanding cryospheric landform inventories – quantitative approaches for underestimated periglacial block- and talus slopes in the Dry Andes of Argentina T. Köhler et al.
118. Exploring the Application of a Debris Flow Likelihood Regression Model in Mediterranean Post-Fire Environments, Using Field Observations-Based Validation M. Diakakis et al.
119. A hybrid machine-learning model to map glacier-related debris flow susceptibility along Gyirong Zangbo watershed under the changing climate C. Qiu et al.
120. Quantifying effectiveness of trees for landslide erosion control R. Spiekermann et al.
121. The impact and interactive effects of multi-level spatial policies on urban renewal: A case study of Shenzhen, China K. Cao & Y. Deng
122. 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.
123. Integration of GIS-MCDA and machine learning for landslide susceptibility mapping to support sustainable land use planning: a case study of Korbous, Tunisia N. Rebai et al.
124. Data‐driven modelling of joint debris flow release susceptibility and connectivity S. Steger et al.
125. A benchmark dataset and workflow for landslide susceptibility zonation M. Alvioli et al.
126. Landslide susceptibility: a statistically-based assessment on a depositional pyroclastic ramp F. Murillo-García et al.
127. Evaluating map quality and model performance through iterative statistics-based landslide susceptibility in eastern KY M. Crawford et al.
128. 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.
129. Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation D. Sun et al.
130. Multivariate geostatistical modeling of the spatial sediment distribution in a large scale drainage basin, Upper Rhone, Switzerland A. Schoch et al.
131. GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models W. Chen et al.
132. When Enough Is Really Enough? On the Minimum Number of Landslides to Build Reliable Susceptibility Models G. Titti et al.
133. Role of Finer Particles in Rheological Characterization of Debris Flows: Insights from Western Himalayas N. Pandey & N. Satyam
134. COĞRAFİ BİLGİ SİSTEMİ TABANLI MANTIKSAL REGRESYON YÖNTEMİ KULLANILARAK ÇUBUK-KALECİK (ANKARA) ŞABANÖZÜ (ÇANKIRI) ARASININ HEYELAN DUYARLILIK DEĞERLENDİRMESİ H. ELMACI et al.
135. Combining spatial modelling and regionalization of rainfall thresholds for debris flows hazard mapping in the Emilia-Romagna Apennines (Italy) C. G. et al.
136. Multi-Temporal Analysis of Forest Fire Probability Using Socio-Economic and Environmental Variables S. Kim et al.
137. 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.
138. National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data Q. Lin et al.