494 citations as recorded by crossref.

1. Landslide susceptibility near highways is increased by 1 order of magnitude in the Andes of southern Ecuador, Loja province A. Brenning et al.
2. Landslide susceptibility mapping using Bayesian approach in the Sultan Mountains (Akşehir, Turkey) A. Ozdemir
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4. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling J. Goetz et al.
5. Validation and evaluation of two multivariate statistical models for predictive shallow landslide susceptibility mapping of the Eastern Pyrenees (Spain) C. Baeza et al.
6. Modeling the potential distribution of shallow-seated landslides using the weights of evidence method and a logistic regression model: a case study of the Sabae Area, Japan R. SONG et al.
7. Landslide susceptibility assessment in the Peloritani Mts. (Sicily, Italy) and clues for tectonic control of relief processes G. De Guidi & S. Scudero
8. Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model D. Camilo et al.
9. Rockfall susceptibility mapping using XGBoost model by hybrid optimized factor screening and hyperparameter H. Wen et al.
10. A spatial–temporal analysis of impacts from human development on the Shih-men Reservoir watershed, Taiwan Y. Chen & K. Chang
11. Landslide Susceptibility Assessment of Heraklion Prefecture in Crete, Greece S. Adam et al.
12. Scientometric Analysis for Spatial Autocorrelation-Related Research from 1991 to 2021 Q. Luo et al.
13. Rainfall-Induced Landslides and Erosion Processes in the Road Network of the Jaén Province (Southern Spain) R. Carpena et al.
14. Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand N. Intarawichian & S. Dasananda
15. Spatial Interpolation of Gravimetric Soil Moisture Using EM38-mk Induction and Ensemble Machine Learning (Case Study from Dry Steppe Zone in Volgograd Region) A. Zeyliger et al.
16. Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy C. Ballabio & S. Sterlacchini
17. Exploring the impact of spatial autocorrelation on optimistic bias in cross-validation and assessing the effectiveness of spatial cross-validation M. Yoo & H. Koo
18. On the use of hierarchical fuzzy inference systems (HFIS) in expert-based landslide susceptibility mapping: the central part of the Rif Mountains (Morocco) B. Ozer et al.
19. The influence of land use and land cover change on landslide susceptibility: a case study in Zhushan Town, Xuan'en County (Hubei, China) L. Chen et al.
20. Landslide susceptibility mapping based on global and local logistic regression models in Three Gorges Reservoir area, China M. Zhang et al.
21. Spatial analysis of groundwater flowing artesian condition using machine learning techniques A. Al-Abadi & J. Alsamaani
22. Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India) I. Das et al.
23. An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method S. Shrestha et al.
24. Estimating the prediction performance of spatial models via spatial k-fold cross validation J. Pohjankukka et al.
25. From scenario-based seismic hazard to scenario-based landslide hazard: fast-forwarding to the future via statistical simulations L. Lombardo & H. Tanyas
26. Integrating machine learning and numerical methods for enhanced landslide susceptibility and hazard mapping in the Bhotekoshi watershed, central Nepal B. Gurung et al.
27. Entropy Ensemble Filter: A Modified Bootstrap Aggregating (Bagging) Procedure to Improve Efficiency in Ensemble Model Simulation H. Foroozand & S. Weijs
28. GIS-based analysis of landslides susceptibility mapping: a case study of Lushoto district, north-eastern Tanzania M. Makonyo & Z. Zahor
29. Evaluating performance of simplified physically based models for shallow landslide susceptibility G. Formetta et al.
30. Utilization of Open Source Spatial Data for Landslide Susceptibility Mapping at Chittagong District of Bangladesh—An Appraisal for Disaster Risk Reduction and Mitigation Approach M. Islam et al.
31. Ensemble classification of paired data W. Adler et al.
32. Tier 4 maps of soil pH at 25 m resolution for the Netherlands A. Helfenstein et al.
33. A new method for determination of most likely landslide initiation points and the evaluation of digital terrain model scale in terrain stability mapping P. Tarolli & D. Tarboton
34. Retrogressive thaw slump susceptibility in the northern hemisphere permafrost region E. Makopoulou et al.
35. Fourth paradigm GIScience? Prospects for automated discovery and explanation from data M. Gahegan
36. 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.
37. A Comparative Assessment of Machine Learning Models for Landslide Susceptibility Mapping in the Rugged Terrain of Northern Pakistan N. Shahzad et al.
38. A review of landslides related to the 2005 Kashmir Earthquake: implication and future challenges M. Basharat et al.
39. Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China W. Chen et al.
40. Spatial cross-validation is not the right way to evaluate map accuracy A. Wadoux et al.
41. Landslide hazard prediction under an extreme rainfall scenario by considering multiple timescale rainfalls and effective recharge Z. Guo et al.
42. Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps S. Steger et al.
43. An evaluation of SVM using polygon-based random sampling in landslide susceptibility mapping: The Candir catchment area (western Antalya, Turkey) B. San
44. Shallow landslide prediction and analysis with risk assessment using a spatial model in a coastal region in the state of São Paulo, Brazil P. Camarinha et al.
45. Landslide susceptibility mapping using physics-guided machine learning: a case study of a debris flow event in Colorado Front Range T. Pei & T. Qiu
46. Effects of rainfall spatial variability and intermittency on shallow landslide triggering patterns at a catchment scale J. von Ruette et al.
47. Supervised classification of landforms in Arctic mountains H. Mithan et al.
48. Landslide susceptibility mapping using deep learning models in Ardabil province, Iran H. Hamedi et al.
49. Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran H. Shahabi et al.
50. ALICE (Assessment of Landslides Induced by Climatic Events): A single tool to integrate shallow and deep landslides for susceptibility and hazard assessment R. Vandromme et al.
51. Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models R. Schlögel et al.
52. Space-time landslide predictive modelling L. Lombardo et al.
53. Landslide Analysis Approaches in Tropical Environment Region for Disaster Risk Reduction M. Kahar et al.
54. Satellite-Based NO2 and Model Validation in a National Prediction Model Based on Universal Kriging and Land-Use Regression M. Young et al.
55. Geographically Weighted Random Forest Based on Spatial Factor Optimization for the Assessment of Landslide Susceptibility F. Lu et al.
56. Simulation of Forest Distribution in the Qilian Mountains of China with a Terrain-based Logistic Regression Model S. Fang et al.
57. Predicting gully occurrence at watershed scale: Comparing topographic indices and multivariate statistical models C. Conoscenti & E. Rotigliano
58. Effects of raster resolution on real probability of landslides X. Shao et al.
59. Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa J. Nsengiyumva et al.
60. A dissimilarity-adaptive cross-validation method for evaluating geospatial machine learning predictions with clustered samples Y. Wang et al.
61. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya K. Devkota et al.
62. A topography‐based model of forest cover at the alpine tree line in the tropical Andes M. Bader & J. Ruijten
63. Distribution Characteristics and Hazard Assessment of Ground Collapse in the Mining Activity Areas of the Turpan–Hami Basin T. Wang et al.
64. Landslide susceptibility mapping with GIS in high mountain area of Nepal: a comparison of four methods P. Gautam et al.
65. 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.
66. Catchment natural driving factors and prediction of baseflow index for Continental United States based on Random Forest technique S. Huang et al.
67. Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine C. Su et al.
68. GIS-based landslide susceptibility mapping with logistic regression, analytical hierarchy process, and combined fuzzy and support vector machine methods: a case study from Wolong Giant Panda Natural Reserve, China Q. Meng et al.
69. Landslide susceptibility assessment in scarce-data regions using remote sensing data E. Aristizábal-Giraldo & D. Ruiz-Vásquez
70. A random forests-based hedonic price model accounting for spatial autocorrelation E. Tepe
71. Landslide susceptibility methodology for railway planning: a comparative analysis of statistical and machine learning methods in a case study of Marche region, Italy R. Rani et al.
72. Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping Y. Wu et al.
73. Geological Hazard Susceptibility Assessment Based on the Combined Weighting Method: A Case Study of Xi’an City, China P. Li et al.
74. Impact of Spatial Resolution of Digital Elevation Model on Landslide Susceptibility Mapping: A Case Study in Kullu Valley, Himalayas S. Meena & T. Gudiyangada Nachappa
75. Improvement of landslide hazard assessments for regulatory zoning in France: STATE–OF–THE-ART perspectives and considerations Y. Thiery et al.
76. Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey M. Loche et al.
77. Is higher resolution always better? A comparison of open-access DEMs for optimized slope unit delineation and regional landslide prediction M. Ahmed et al.
78. Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models A. Arora et al.
79. Spatial analysis of ecological risk in a coastal dune landscape using high resolution aerial photography N. ListyaNiNgrum et al.
80. Random Forest with semantic tie points for classifying landforms and creating rigorous shaded relief representations F. Veronesi & L. Hurni
81. Comprehensive performance assessment of landslide susceptibility mapping with MLP and random forest: a case study after Elazig earthquake (24 Jan 2020, Mw 6.8), Turkey G. Karakas et al.
82. Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia A. Youssef et al.
83. Combining release and runout in statistical landslide susceptibility modeling M. Mergili et al.
84. Spatial and temporal variability of soil saturated hydraulic conductivity in gradients of disturbance B. Zimmermann & H. Elsenbeer
85. Hierarchical machine learning for the automatic classification of surface deformation from SAR observations J. Rivera-Rivera et al.
86. Source areas, connectivity, and delivery rate of sediments in mountainous-forested hillslopes: A probabilistic approach A. Cislaghi & G. Bischetti
87. Do landslides follow landslides? Insights in path dependency from a multi-temporal landslide inventory J. Samia et al.
88. Regional-scale landslide risk assessment in Central Asia F. Caleca et al.
89. Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows T. Heckmann et al.
90. 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.
91. Data-driven mapping of the potential mountain permafrost distribution N. Deluigi et al.
92. Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods H. Hong et al.
93. Transformer, more than meets the eye: A deep learning approach to integrate rainfall time-series in multi-type landslide probability modelling R. Rani et al.
94. How can statistical models help to determine driving factors of landslides? P. Vorpahl et al.
95. Flow-Py v1.0: a customizable, open-source simulation tool to estimate runout and intensity of gravitational mass flows C. D'Amboise et al.
96. Spatial landslide hazard assessment along the Jelapang Corridor of the North-South Expressway in Malaysia using high resolution airborne LiDAR data N. Yusof et al.
97. Spatial Non-Stationarity-Based Landslide Susceptibility Assessment Using PCAMGWR Model Y. Li et al.
98. Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study Á. Felicísimo et al.
99. A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam) D. Bui et al.
100. 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.
101. Effects of sample size on the accuracy of geomorphological models J. Hjort & M. Marmion
102. Landslide susceptibility evaluation and hazard zonation techniques – a review L. Shano et al.
103. Comparison of machine learning algorithms for soil salinity predictions in three dryland oases located in Xinjiang Uyghur Autonomous Region (XJUAR) of China F. Wang et al.
104. Soil erodibility assessment of laterite dominant sub-basin watersheds in the humid tropical region of India A. Gayen & S. Mafizul Haque
105. Effect of Earthquake on Subsequent Typhoon-Induced Landslides Using Remote Sensing Imagery in the 99 Peaks Region, Central Taiwan S. Lee et al.
106. Multi-Temporal Landslide Inventory-Based Statistical Susceptibility Modeling Associated With the 2017 Mw 6.5 Jiuzhaigou Earthquake, Sichuan, China L. Luo et al.
107. A Tool for Classification and Regression Using Random Forest Methodology: Applications to Landslide Susceptibility Mapping and Soil Thickness Modeling D. Lagomarsino et al.
108. A Comparative Analysis of Machine Learning Techniques for National Glacier Mapping: Evaluating Performance through Spatial Cross-Validation in Perú M. Bueno et al.
109. Applying GIS to monitor school exposure to landslide hazards in disaster-prone areas of Mount Elgon in Uganda F. Akello et al.
110. Predicting Soil Erodibility Using Electrical Resistivity Tomography M. Karim & S. Tucker-Kulesza
111. A hybrid random forests and artificial neural networks bagging ensemble for landslide susceptibility modelling L. Lucchese et al.
112. Predicting species abundance using machine learning approach: a comparative assessment of random forest spatial variants and performance metrics C. Mushagalusa et al.
113. Estimation of erosion in tectonically active orogenies. Example from the Bhotekoshi catchment, Himalaya (Nepal) C. Andermann & R. Gloaguen
114. Evaluation of Machine Learning Performance in Wildfire Susceptibility Mapping Under Limited Training Data Condition M. SHAFAPOURTEHRANY
115. Optimizing a prediction model for engineering disturbance hazard susceptibility along the China-Nepal highway in the Qinghai-Tibet plateau L. Li et al.
116. Assessing the Available Landslide Susceptibility Map and Inventory for the Municipality of Rio de Janeiro, Brazil: Potentials and Challenges for Data-Driven Applications P. Lima et al.
117. A new simple topo-climatic model to predict surface displacement in paraglacial and periglacial mountains of the European Alps: The importance of ground heating index and floristic components as ecological indicators S. Ponti et al.
118. Application of an evidential belief function model in landslide susceptibility mapping O. Althuwaynee et al.
119. Shallow landslides susceptibility assessment in different environments M. Persichillo et al.
120. A functional entity approach to predict soil erosion processes in a small Plio-Pleistocene Mediterranean catchment in Northern Chianti, Italy M. Märker et al.
121. Landslide hazard mapping using GIS and weight of evidence model in Qingshui River watershed of 2008 Wenchuan earthquake struck region C. Xu et al.
122. Multifactor empirical mapping of the protective function of forests against landslide occurrence: statistical approaches and a case study D. Cimini et al.
123. Effect of landslide spatial representation and raster resolution on the landslide susceptibility assessment S. Yang et al.
124. 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
125. A novel voting ensemble model for spatial prediction of landslides using GIS S. Sachdeva et al.
126. Combination of statistical and conceptual approaches for debris-flow susceptibility modelling at a regional scale, British Columbia, Canada T. Bornaetxea et al.
127. Forest cover change trajectories and their impact on landslide occurrence in the tropical Andes M. Guns & V. Vanacker
128. A free web-based approach for rainfall-induced landslide susceptibility modeling: Case study of Clearwater National Forest, Idaho, USA P. Gorsevski
129. Chinese High Resolution Satellite Data and GIS-Based Assessment of Landslide Susceptibility along Highway G30 in Guozigou Valley Using Logistic Regression and MaxEnt Model Y. Liu et al.
130. Planet Image-Based Inventorying and Machine Learning-Based Susceptibility Mapping for the Landslides Triggered by the 2018 Mw6.6 Tomakomai, Japan Earthquake X. Shao et al.
131. A step beyond landslide susceptibility maps: a simple method to investigate and explain the different outcomes obtained by different approaches T. Xiao et al.
132. New insights into machine learning prediction techniques for real-time sanitary risk assessment in karst drinking water sources affected by faecal contamination J. Fernández-Ortega et al.
133. Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey H. Akinci & M. Zeybek
134. The propagation of inventory-based positional errors into statistical landslide susceptibility models S. Steger et al.
135. Evaluation of predictive models for post-fire debris flow occurrence in the western United States E. Nikolopoulos et al.
136. Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg–Marquardt and Bayesian regularized neural networks D. Tien Bui et al.
137. Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): A comparative assessment of the efficacy of evidential belief functions and fuzzy logic models D. Tien Bui et al.
138. Kernel-Based Mapping of Orographic Rainfall Enhancement in the Swiss Alps as Detected by Weather Radar L. Foresti et al.
139. Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction H. Meyer et al.
140. iSoLIM : a similarity-based spatial prediction software for the big data era F. Zhao et al.
141. Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy) D. Costanzo et al.
142. Multi-Resolution Landslide Susceptibility Analysis Using a DEM and Random Forest U. Paudel et al.
143. Random Forest-Based Landslide Susceptibility Mapping in Coastal Regions of Artvin, Turkey H. Akinci et al.
144. Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution A. Shirzadi et al.
145. Big Data Analytics for Emergency Communication Networks: A Survey J. Wang et al.
146. Sedimentological characteristics and application of machine learning techniques for landslide susceptibility modelling along the highway corridor Nahan to Rajgarh (Himachal Pradesh), India V. Pandey et al.
147. Combination of Rainfall Thresholds and Susceptibility Maps for Dynamic Landslide Hazard Assessment at Regional Scale S. Segoni et al.
148. Development and validation of sinkhole susceptibility models in mantled karst settings. A case study from the Ebro valley evaporite karst (NE Spain) J. Galve et al.
149. Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: A case of the Belice River basin (western Sicily, Italy) C. Conoscenti et al.
150. A grid portal for solving geoscience problems using distributed knowledge discovery services G. Folino et al.
151. Introducing a novel multi-layer perceptron network based on stochastic gradient descent optimized by a meta-heuristic algorithm for landslide susceptibility mapping H. Hong et al.
152. Landslide susceptibility: a statistically-based assessment on a depositional pyroclastic ramp F. Murillo-García et al.
153. Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting A. van Natijne et al.
154. Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model D. Tien Bui et al.
155. Regional-scale controls on the spatial activity of rockfalls (Turtmann Valley, Swiss Alps) — A multivariate modeling approach K. Messenzehl et al.
156. Integration of rainfall thresholds and susceptibility maps in the Emilia Romagna (Italy) regional-scale landslide warning system S. Segoni et al.
157. Broad-scale species distribution models applied to data-poor areas C. Guillaumot et al.
158. A Physically Constrained Variational Autoencoder for Geochemical Pattern Recognition Y. Xiong et al.
159. Lethality level analysis of secondary landslides based on field survey data: a case study of Luding earthquake L. Yao et al.
160. Landslide Susceptibility Assessment in Nepal’s Chure Region: A Geospatial Analysis P. Thapa et al.
161. Landslide susceptibility mapping at sin Ho, Lai Chau province, Vietnam using ensemble models based on fuzzy unordered rules induction algorithm T. Bien et al.
162. PSLSA v2.0: An automatic Python package integrating machine learning models for regional landslide susceptibility assessment Z. Guo et al.
163. Application of gis and frequency ratio model in mapping the potential surface failure sites in the poonjar sub-watershed of meenachil river in western ghats of kerala H. Vijith & G. Madhu
164. Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy S. Segoni et al.
165. Transferability of regional permafrost disturbance susceptibility modelling using generalized linear and generalized additive models A. Rudy et al.
166. Scale Effects in Landslide Susceptibility Assessment: Integrating Slope Unit Division and SHAP-Based Interpretability in a Typical River Basin W. Hu et al.
167. A review on spatial, temporal and magnitude prediction of landslide hazard A. Tyagi et al.
168. Spatial prediction of spring locations in data poor region of Central Himalayas R. Niraula et al.
169. Optimizing and validating the Gravitational Process Path model for regional debris-flow runout modelling J. Goetz et al.
170. Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan J. Dou et al.
171. Comparing methods of landslide data acquisition and susceptibility modelling: Examples from New Zealand H. Smith et al.
172. A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping A. Zhu et al.
173. Spatial statistical modeling of shallow landslides—Validating predictions for different landslide inventories and rainfall events J. von Ruette et al.
174. Landslide Susceptibility Mapping Using Machine Learning: A Danish Case Study A. Ageenko et al.
175. Shallow-landslide stability evaluation in loess areas according to the Revised Infinite Slope Model: a case study of the 7.25 Tianshui sliding-flow landslide events of 2013 in the southwest of the Loess Plateau, China J. Zhuang et al.
176. Assessment of rainfall-induced landslide susceptibility in Artvin, Turkey using machine learning techniques H. Akinci
177. The Effect of Observation Data Sampling Methods on Infiltration Areas by Maximum Entropy Model M. Jafarzadeh et al.
178. Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility P. Lima et al.
179. Field Measurements of Passive Earth Forces in Steep, Shallow, Landslide‐Prone Areas A. Cislaghi et al.
180. Review on landslide susceptibility mapping using support vector machines Y. Huang & L. Zhao
181. Comprehensive landslide susceptibility map of Central Asia A. Rosi et al.
182. Uncertainties of landslide susceptibility prediction considering different landslide types F. Huang et al.
183. Application of a Novel Hybrid Machine Learning Algorithm in Shallow Landslide Susceptibility Mapping in a Mountainous Area B. Ghasemian et al.
184. Building a landslide hazard indicator with machine learning and land surface models T. Stanley et al.
185. Active-Learning Approaches for Landslide Mapping Using Support Vector Machines Z. Wang & A. Brenning
186. Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: Comparison of a new method to calculate weighting factors by means of bivariate statistics M. Meinhardt et al.
187. Catchment-scale impacts of shallow landslides on stream water chemistry N. Yoshihara et al.
188. Landslide susceptibility mapping on a global scale using the method of logistic regression L. Lin et al.
189. Quantifying effectiveness of trees for landslide erosion control R. Spiekermann et al.
190. Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results V. Capecchi et al.
191. Multi-scale support vector algorithms for hot spot detection and modelling A. Pozdnoukhov & M. Kanevski
192. Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling J. Lai et al.
193. Assessing Landslide Drivers in Social–Ecological–Technological Systems: The Case of Metropolitan Region of São Paulo, Brazil M. Hirye et al.
194. Transferability of geomorphological distribution models: Evaluation using solifluction features in subarctic and Arctic regions J. Hjort et al.
195. How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? — A catchment-scale case study from China Z. Guo et al.
196. A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia H. Shahabi et al.
197. Exploring performance and robustness of shallow landslide susceptibility modeling at regional scale using different training and testing sets M. Conforti et al.
198. Developing a more complete understanding of tropical montane forest disturbance ecology through landslide research C. Freund & M. Silman
199. Landslide susceptibility assessment in complex geological settings: sensitivity to geological information and insights on its parameterization S. Segoni et al.
200. Integration of the Statistical Index Method and the Analytic Hierarchy Process technique for the assessment of landslide susceptibility in Huizhou, China G. Zhang et al.
201. Assessment of co-seismic landslide hazard using the Newmark model and statistical analyses: a case study of the 2013 Lushan, China, Mw6.6 earthquake S. Ma & C. Xu
202. Coğrafi Bilgi Sistemleri ile Suşehri (Sivas) Heyelan Duyarlılık Analizi G. DEMİR
203. On the use of adversarial validation for quantifying dissimilarity in geospatial machine learning prediction Y. Wang et al.
204. Spatially distributed antecedent rainfall thresholds for landslide occurrence: a multitask machine learning modelling approach L. Lucchese et al.
205. Hybrid meta-heuristic machine learning methods applied to landslide susceptibility mapping in the Sahel-Algiers M. Benbouras
206. Engineered slope failure susceptibility modelling using high spatial resolution geospatial data S. Obrike et al.
207. When Enough Is Really Enough? On the Minimum Number of Landslides to Build Reliable Susceptibility Models G. Titti et al.
208. Using InfoVal method and GIS techniques for the spatial modelling of landslide susceptibility in the upper catchment of river Meenachil in Kerala H. Vijith et al.
209. Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China C. Xu et al.
210. Joint modelling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions R. Yadav et al.
211. The performance of landslide susceptibility models critically depends on the quality of digital elevation models J. Brock et al.
212. Exploring the effect of absence selection on landslide susceptibility models: A case study in Sicily, Italy C. Conoscenti et al.
213. Foundation for unbiased cross-validation of spatio-temporal models for Species Distribution Modeling D. Koldasbayeva & A. Zaytsev
214. Spatial+: A new cross-validation method to evaluate geospatial machine learning models Y. Wang et al.
215. A review of regularised estimation methods and cross-validation in spatiotemporal statistics P. Otto et al.
216. Fiber bundle model for multiscale modeling of hydromechanical triggering of shallow landslides D. Cohen et al.
217. Advancing rainfall-induced landslide hazard mapping: comparative study of semi-quantitative, statistical, hybrid, and deterministic models T. Nontapot & K. Chao
218. Integrating Eco-DRR into landslide susceptibility assessment: The critical role of eco-environmental factors M. Broquet et al.
219. Evaluation of Different Landslide Susceptibility Models for a Local Scale in the Chitral District, Northern Pakistan B. Aslam et al.
220. How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Y. Achour & H. Pourghasemi
221. Landslide Susceptibility Assessment in Ya’an Based on Coupling of GWR and TabNet J. Li et al.
222. Disaggregating County-Level Census Data for Population Mapping Using Residential Geo-Objects With Multisource Geo-Spatial Data T. Wu et al.
223. Landslide susceptibility mapping in Lesalso (Laelay Maichew), Northern Ethiopia: a GIS approach using frequency ratio and analytical hierarchy process methods A. Gebrehiwot et al.
224. A framework for estimating actual evapotranspiration at weather stations without flux observations by combining data from MODIS and flux towers through a machine learning approach C. Zhang et al.
225. A spatially aware Bayesian deep learning framework for UAV-based soil salinity prediction D. Dayal et al.
226. Landslide failure and runout susceptibility in the upper T. Ceno valley (Northern Apennines, Italy) A. Clerici et al.
227. Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River Basin (Sicily, Italy) S. Angileri et al.
228. Evaluation and Validation of Landslide Spatial Susceptibility in the Western Ghats of Kerala, through GIS-based Weights of Evidence Model and Area under Curve Technique V. Prasannakumar & H. Vijith
229. Swarm optimization based heterogeneous machine learning techniques for enhanced landslide susceptibility assessment with comprehensive uncertainty quantification S. Dey & S. Das
230. Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India D. Kumar et al.
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232. Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016) H. Pourghasemi et al.
233. A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models D. Davoudi Moghaddam et al.
234. Evaluating the uncertainty in landslide susceptibility prediction: effect of spatial data variability and evaluation unit choices S. Qin et al.
235. Prediction of the landslide susceptibility: Which algorithm, which precision? H. Pourghasemi & O. Rahmati
236. GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China Y. Zhao et al.
237. GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China C. Xu et al.
238. Landslide Susceptibility Zoning Using C5.0 Decision Tree, Random Forest, Support Vector Machine and Comparison of Their Performance in a Coal Mine Area Q. Su et al.
239. Landslide occurrence and its relation with terrain factors in the Siwalik Hills, Nepal: case study of susceptibility assessment in three basins M. Ghimire
240. Exploring the impact of introducing the TRIGRS physical model into machine learning model on the rainfall-induced shallow landslide-susceptibility assessment L. Li et al.
241. Landslide susceptibility assessment using SVM machine learning algorithm M. Marjanović et al.
242. Modeling the spatial distribution of subarctic forest in northern Manitoba using GIS-based terrain and climate data S. Mamet et al.
243. A methodological approach of QRA for slow-moving landslides at a regional scale F. Caleca et al.
244. An Ensemble Approach of Feature Selection and Machine Learning Models for Regional Landslide Susceptibility Mapping in the Arid Mountainous Terrain of Southern Peru C. Kumar et al.
245. Analytical techniques for mapping multi-hazard with geo-environmental modeling approaches and UAV images N. Kariminejad et al.
246. Predicting Earthquake-Induced Landslides by Using a Stochastic Modeling Approach: A Case Study of the 2001 El Salvador Coseismic Landslides C. Mercurio et al.
247. Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization K. Khosravi et al.
248. Optimizing Landslide Susceptibility Mapping with Non-Landslide Sampling Strategy and Spatio-Temporal Fusion Models J. Deng et al.
249. Métodos estadísticos para la evaluación de la susceptibilidad por movimientos en masa E. Aristizábal-Giraldo et al.
250. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy) A. Trigila et al.
251. 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.
252. A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods A. Zhu et al.
253. Classification Efficacy Using K-Fold Cross-Validation and Bootstrapping Resampling Techniques on the Example of Mapping Complex Gully Systems K. Phinzi et al.
254. Integrating geomorphology, statistic and numerical simulations for landslide invasion hazard scenarios mapping: An example in the Sorrento Peninsula (Italy) F. Lucà et al.
255. Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression I. Colkesen et al.
256. A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020 J. Huang et al.
257. To what extent do natural disturbances contribute to Andean plant diversity? A theoretical outline from the wettest and driest parts of the tropical Andes M. Richter
258. An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm A. Akgun et al.
259. On the use of explainable AI for susceptibility modeling: Examining the spatial pattern of SHAP values N. Wang et al.
260. Evaluation of Landslide Risk Using the WoE and IV Methods: A Case Study in the Zipaquirá–Pacho Road Corridor S. Velazco et al.
261. Estimating the Quality of the Most Popular Machine Learning Algorithms for Landslide Susceptibility Mapping in 2018 Mw 7.5 Palu Earthquake S. Ma et al.
262. GIS-based bivariate statistical modelling for earthquake-triggered landslides susceptibility mapping related to the 2008 Wenchuan earthquake, China C. Xu et al.
263. An introduction to learning algorithms and potential applications in geomorphometry and Earth surface dynamics A. Valentine & L. Kalnins
264. Improving sinkhole hazard models incorporating magnitude–frequency relationships and nearest neighbor analysis J. Galve et al.
265. Identifying the controls on coastal cliff landslides using machine-learning approaches M. Dickson & G. Perry
266. Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system M. Ahmed et al.
267. Integrating predictive modeling techniques with geospatial data for landslide susceptibility assessment in northern Pakistan A. Asghar et al.
268. Evaluating map quality and model performance through iterative statistics-based landslide susceptibility in eastern KY M. Crawford et al.
269. Landslide susceptibility assessment using the certainty factor and deep neural network W. Ma et al.
270. Spatial machine-learning model diagnostics: a model-agnostic distance-based approach A. Brenning
271. Spatial prediction strategy for landslides triggered by large earthquakes oriented to emergency response, mid-term resettlement and later reconstruction S. Ma et al.
272. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression T. Kavzoglu et al.
273. Improved environmental mapping and validation using bagging models with spatially clustered data B. Misiuk & C. Brown
274. Electrical resistivity as a binary classifier for bridge scour evaluation M. Karim et al.
275. Evolution of soil wetting patterns preceding a hydrologically induced landslide inferred from electrical resistivity survey and point measurements of volumetric water content and pore water pressure P. Lehmann et al.
276. Spatial statistics and soil mapping: A blossoming partnership under pressure G. Heuvelink & R. Webster
277. Indirect modeling of hourly meteorological time series for winter road maintenance A. Brenning et al.
278. Assessment and validation of GIS-based landslide susceptibility maps: a case study from Feltrino stream basin (Central Italy) M. Sciarra et al.
279. The 2010 Yushu earthquake triggered landslide hazard mapping using GIS and weight of evidence modeling C. Xu et al.
280. Seismically induced landslide hazard and exposure modelling in Southern California based on the 1994 Northridge, California earthquake event M. Budimir et al.
281. Effect of bin width on variogram model accuracy: a case study of teak tree volume specific to Solomon clone in Tawau, Sabah, Malaysia J. Jamalul Kiram et al.
282. Using machine learning paradigm to predict CBR value of treated expansive soil U. Ahmad et al.
283. Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression M. García-Rodríguez et al.
284. Spatial leave‐one‐out cross‐validation for variable selection in the presence of spatial autocorrelation K. Le Rest et al.
285. A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin, China Z. Xie et al.
286. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree D. Tien Bui et al.
287. Comparisons of filter, wrapper, and embedded feature selection for rockfall susceptibility prediction and mapping C. Lei et al.
288. Benchmarking data handling strategies for landslide susceptibility modeling using random forest workflows G. Samodra et al.
289. Mapping flood susceptibility in an arid region of southern Iraq using ensemble machine learning classifiers: a comparative study A. Al-Abadi
290. Integration of multi-criteria decision analysis and statistical models for landslide susceptibility mapping in the western Algiers Province (Algeria) using GIS techniques and remote sensing data S. Mokadem et al.
291. A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS T. Chen et al.
292. Macroscopic Lane Change Model—A Flexible Event-Tree-Based Approach for the Prediction of Lane Change on Freeway Traffic C. Ng et al.
293. 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
294. The use of digital technologies for landslide disaster risk research and disaster risk management: progress and prospects H. Bao et al.
295. Three oversampling methods applied in a comparative landslide spatial research in Penang Island, Malaysia H. Gao et al.
296. Field-based landslide susceptibility assessment in a data-scarce environment: the populated areas of the Rwenzori Mountains L. Jacobs et al.
297. Development of a morphometric connectivity model to mitigate sediment derived from storm-driven shallow landslides R. Spiekermann et al.
298. A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China W. Xie et al.
299. Spatial Regression Models for Field Trials: A Comparative Study and New Ideas S. Hawinkel et al.
300. Performance assessment of the landslide susceptibility modelling using the support vector machine, radial basis function network, and weight of evidence models in the N'fis river basin, Morocco H. Naceur et al.
301. Comparative analysis of certainty factor and analytic hierarchy process for landslide susceptibility zonation in parts of Solan, Himachal Pradesh, India A. Magray et al.
302. Context-sensitive analysis of disaster resilience and equity through geospatial explainable machine learning Y. Ding et al.
303. Incorporation of neighborhood information improves performance of SDB models A. Knudby & G. Richardson
304. Landslide susceptibility mapping by using a geographic information system (GIS) along the China–Pakistan Economic Corridor (Karakoram Highway), Pakistan S. Ali et al.
305. Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway) N. Nocentini et al.
306. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine I. Yilmaz
307. A software framework for classification models of geographical data Y. Liu et al.
308. Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster L. Lombardo et al.
309. Development of a binary logistic lane change model and its validation using empirical freeway data C. Ng et al.
310. Landslide susceptibility mapping using AHP and fuzzy methods in the Gilan province, Iran Y. Bahrami et al.
311. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran H. Pourghasemi et al.
312. Reducing subjectivity in landslide susceptibility mapping method: an integrated methodology S. Gebru & T. Shewangzaw
313. Soft computing in assessment of earthquake-triggered landslide susceptibility C. Xu et al.
314. A hybrid machine learning model for landslide-oriented risk assessment of long-distance pipelines H. Wen et al.
315. Pre-processing algorithms and landslide modelling on remotely sensed DEMs M. Santini et al.
316. Data-driven topo-climatic mapping with machine learning methods A. Pozdnoukhov et al.
317. Comparison of new individual and hybrid machine learning algorithms for modeling and mapping fire hazard: a supplementary analysis of fire hazard in different counties of Golestan Province in Iran S. Eskandari et al.
318. The influence of systematically incomplete shallow landslide inventories on statistical susceptibility models and suggestions for improvements S. Steger et al.
319. The RUSLE erosion index as a proxy indicator for debris flow susceptibility A. Zini et al.
320. Towards landslide space-time forecasting through machine learning: the influence of rainfall parameters and model setting N. Nocentini et al.
321. Mapping Fractional Land Use and Land Cover in a Monsoon Region: The Effects of Data Processing Options B. Seo et al.
322. A quick method of early landslide identification based on dynamic susceptibility analysis using M-SVM method: a case study Y. Liu et al.
323. Comparison and combination of different models for optimal landslide susceptibility zonation G. Chen et al.
324. Susceptibility mapping of instability related to shallow mining cavities in a built-up environment R. Pellicani et al.
325. Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function–logistic regression algorithm A. Arabameri et al.
326. Assessment of calanchi and rill-interrill erosion susceptibility in northern Liguria, Italy: A case study using a probabilistic modelling framework M. Maerker et al.
327. Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messina (Sicily, southern Italy) L. Lombardo et al.
328. A Comparative Study of the Frequency Ratio, Analytical Hierarchy Process, Artificial Neural Networks and Fuzzy Logic Methods for Landslide Susceptibility Mapping: Taşkent (Konya), Turkey A. Ozdemir
329. A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery L. Xu et al.
330. Evaluation of geological risk and hydrocarbon favorability using logistic regression model with case study Z. Zhu et al.
331. Exploring the impact of introducing a physical model into statistical methods on the evaluation of regional scale debris flow susceptibility J. Sun et al.
332. Comparison of Statistical Approaches for Modelling Land-Use Change B. Sun & D. Robinson
333. Geomorphic process rates of landslides along a humidity gradient in the tropical Andes J. Muenchow et al.
334. Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area Q. Su et al.
335. Uncertainty due to DEM error in landslide susceptibility mapping C. Qin et al.
336. Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping L. Lucchese et al.
337. Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression D. Bui et al.
338. Terrain Controls and Landscape-Scale Susceptibility Modelling of Active-Layer Detachments, Sabine Peninsula, Melville Island, Nunavut A. Rudy et al.
339. Benchmarking classifiers to optimally integrate terrain analysis and multispectral remote sensing in automatic rock glacier detection A. Brenning
340. Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China C. Xu et al.
341. Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models D. Tien Bui et al.
342. Distinctive controls on the distribution of river-damming and non-damming landslides induced by the 2008 Wenchuan earthquake R. Tang et al.
343. A systematic review of landslide probability mapping using logistic regression M. Budimir et al.
344. Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods B. Pham et al.
345. GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models W. Chen et al.
346. Assessing the utility of regionalized rock-mass geomechanical properties in rockfall susceptibility modelling in an alpine environment G. Bajni et al.
347. Integrating physical and empirical landslide susceptibility models using generalized additive models J. Goetz et al.
348. Landslide susceptibility of the Prato–Pistoia–Lucca provinces, Tuscany, Italy S. Segoni et al.
349. Degree of geocomplexity for diagnosing spatial pattern loss beyond prediction accuracy H. Liu et al.
350. 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.
351. Autologistic modelling of susceptibility to landsliding in the Central Apennines, Italy P. Atkinson & R. Massari
352. Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria P. Lima et al.
353. A comparative study of different machine learning models for landslide susceptibility prediction: a case study of Kullu-to-Rohtang pass transport corridor, India . Nirbhav et al.
354. Landslide susceptibility prediction and mapping in Loess Plateau based on different machine learning algorithms by hybrid factors screening: Case study of Xunyi County, Shaanxi Province, China X. Liu et al.
355. Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek, South Korea A. Pradhan & Y. Kim
356. An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic A. Zhu et al.
357. A side-sampling based Linformer model for landslide susceptibility assessment: a case study of the railways in China N. Jiang et al.
358. Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data P. Schratz et al.
359. An assessment on the use of Terra ASTER L3A data in landslide susceptibility mapping H. Nefeslioglu et al.
360. Improving the reliability of landslide susceptibility mapping through spatial uncertainty analysis: a case study of Al Hoceima, Northern Morocco H. Rahali
361. Dynamic association of slope movements in the Uttarakhand Himalaya: a critical review on the landslide susceptibility assessment H. Khali et al.
362. Soybean yield prediction using machine learning algorithms under a cover crop management system L. Santos et al.
363. Effect of Various Feature Engineering Techniques and Hyper-Parameters Tuning of Machine Learning Approaches for Landslide Susceptibility Map Generation V. Saxena et al.
364. Reasons for an outstanding plant diversity in the tropical Andes of Southern Ecuador M. Richter et al.
365. Manifestation of LiDAR-Derived Parameters in the Spatial Prediction of Landslides Using Novel Ensemble Evidential Belief Functions and Support Vector Machine Models in GIS M. Jebur et al.
366. Improving transferability strategies for debris flow susceptibility assessment: Application to the Saponara and Itala catchments (Messina, Italy) M. Cama et al.
367. A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses B. Pradhan et al.
368. Scaling land-surface variables for landslide detection F. Sîrbu et al.
369. Climate Change Induced Landslide Susceptibility Assessment - for Aiding Climate Resilient Planning for Road Infrastructure: A Case Study in Rangamati District, Chittagong Hill Tracts, Bangladesh I. Wijaya et al.
370. Modelling terrain erosion susceptibility of logged and regenerated forested region in northern Borneo through the Analytical Hierarchy Process (AHP) and GIS techniques H. Vijith & D. Dodge-Wan
371. Quantitative analysis of landslide impact on vegetation: Insights from field surveys and UAV imagery S. Yousefi et al.
372. Method for prediction of landslide movements based on random forests M. Krkač et al.
373. A remote sensing-based tool for assessing rainfall-driven hazards D. Wright et al.
374. Landslide susceptibility mapping using precipitation data, Mazandaran Province, north of Iran M. Arab Amiri & C. Conoscenti
375. Comparative Assessment of Quantitative Landslide Susceptibility Mapping Using Feature Selection Techniques B. Joshi et al.
376. Spatio-Statistical Analysis of Flood Susceptibility Assessment Using Bivariate Model in the Floodplain of River Swat, District Charsadda, Pakistan M. Moazzam et al.
377. How can landslide risk maps be validated? Potential solutions with open-source databases F. Caleca et al.
378. Landslide susceptibility assessment of the Youfang catchment using logistic regression S. Bai et al.
379. Machine learning and multi-sensor based modelling of woody vegetation in the Molopo Area, South Africa M. Ludwig et al.
380. Spatial modeling of multi-hazard threat to cultural heritage sites L. Lombardo et al.
381. Evaluation of the influence of source and spatial resolution of DEMs on derivative products used in landslide mapping R. Mahalingam & M. Olsen
382. Markov–Switching Spatio–Temporal Generalized Additive Model for Landslide Susceptibility A. Sridharan et al.
383. Qualitative Model for Hurricane-Induced Debris Flow Prediction: A Case Study of the Impact of Hurricane Maria (2017) in Puerto Rico Y. Gorokhovich et al.
384. A review of statistically-based landslide susceptibility models P. Reichenbach et al.
385. Assessment of Landslide Susceptibility Using Different Machine Learning Methods in Longnan City, China J. Gao et al.
386. Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis K. Hu et al.
387. Effects of forest cover, topography, and sampling extent on the measured density of shallow, translational landslides D. Miller & K. Burnett
388. Cost-based analysis of mitigation measures for shallow-landslide risk reduction strategies J. Galve et al.
389. Evaluation of ground conditions and vehicle-induced vibration using Spatio-geophysical approach for the Nilgiris landslide transportation interaction corridor, Western Ghats, India S. Chellamuthu & G. Ganapathy
390. Rapid susceptibility mapping of co-seismic landslides triggered by the 2013 Lushan Earthquake using the regression model developed for the 2008 Wenchuan Earthquake W. Li et al.
391. Spatial Prediction Models for Seismic Landslides Based on Support Vector Machine and Varied Kernel Functions: A Case Study of the 14 April 2010 Yushu Earthquake in China
392. Application of expert rules in indirect approaches for landslide susceptibility assessment Y. Thiery et al.
393. Modeling shallow landslides and root reinforcement: A review I. Murgia et al.
394. Early identification of an impending rockslide location via a spatially-aided Gaussian mixture model S. Zhou et al.
395. Estimating global landslide susceptibility and its uncertainty through ensemble modeling A. Felsberg et al.
396. Setting up evaluate indicators for slope control engineering based on spatial clustering analysis W. Wang
397. Spatiotemporal distribution of labeled data can bias the validation and selection of supervised learning algorithms: A marine remote sensing example A. Stock
398. Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach J. Deppner & M. Cajias
399. Landslide susceptibility mapping using GIS and weighted overlay method: a case study from NW Himalayas, Pakistan M. Basharat et al.
400. Focusing on the spatial non-stationarity of landslide predisposing factors in northern Iceland T. Feuillet et al.
401. An inventory-driven rock glacier status model (intact vs. relict) for South Tyrol, Eastern Italian Alps C. Kofler et al.
402. Landslide susceptibility mapping along national highway-1 in Jammu and Kashmir State (India) G. Hussain et al.
403. Landslide intelligent prediction using object-oriented method X. Wang & R. Niu
404. Functional soil-landscape modelling to estimate slope stability in a steep Andean mountain forest region M. Ließ et al.
405. Comparison of general kernel, multiple kernel, infinite ensemble and semi-supervised support vector machines for landslide susceptibility prediction Z. Fang et al.
406. Comparison of spatial and aspatial logistic regression models for landmine risk mapping C. Schultz et al.
407. A hybrid-optimized Random Forest interpretable model for debris flow susceptibility by prior model-based negative sampling H. Wen et al.
408. Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment H. Li et al.
409. Landslides in Tijuana, Mexico: hazard assessment in an urban neighborhood A. Oliva González et al.
410. Review on remote sensing methods for landslide detection using machine and deep learning A. Mohan et al.
411. Landslide Hazard and Exposure Modelling in Data‐Poor Regions: The Example of the Rohingya Refugee Camps in Bangladesh R. Emberson et al.
412. GIS-Based Landslide Susceptibility Mapping for Land Use Planning and Risk Assessment A. Roccati et al.
413. Potential Controlling Factors and Landslide Susceptibility Features of the 2022 Ms 6.8 Luding Earthquake S. Ma et al.
414. Landslide Susceptibility Assessment Using a CNN–BiLSTM-AM Model X. Ju et al.
415. What controls the distribution of post-Little Ice Age landslides around the South Patagonian Icefield? G. Seier et al.
416. Replication of a terrain stability mapping using an Artificial Neural Network M. Pavel et al.
417. Spatial clustering and modelling for landslide susceptibility mapping in the north of the Kathmandu Valley, Nepal B. Pokharel et al.
418. Integration of Analytical Hierarchy Process and Landslide Susceptibility Index Based Landslide Susceptibility Assessment of the Pearl River Delta Area, China H. Zhang et al.
419. Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping N. Micheletti et al.
420. A global slope unit-based method for the near real-time prediction of earthquake-induced landslides H. Tanyas et al.
421. Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy) M. Cama et al.
422. Application of different earthquake-induced landslide hazard assessment models on the 2022 Ms 6.8 luding earthquake Y. Lu et al.
423. Logistic regression applied to natural hazards: rare event logistic regression with replications M. Guns & V. Vanacker
424. A Novel Relevance Vector Machine Classifier with Cuckoo Search Optimization for Spatial Prediction of Landslides N. Hoang & D. Tien Bui
425. A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran) S. Emami et al.
426. A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland M. Marmion et al.
427. Assessing the quality of landslide susceptibility maps – case study Lower Austria H. Petschko et al.
428. On the dangers of model complexity without ecological justification in species distribution modeling D. Bell & D. Schlaepfer
429. Community perceptions of landslide risk and susceptibility: a multi-country study M. Bukhari et al.
430. Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues F. Catani et al.
431. Integration of Remotely Sensed Soil Sealing Data in Landslide Susceptibility Mapping T. Luti et al.
432. Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS D. Tien Bui et al.
433. Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale S. Oliveira et al.
434. GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms S. Ali et al.
435. A test of transferability for landslides susceptibility models under extreme climatic events: application to the Messina 2009 disaster L. Lombardo et al.
436. Reliability of groundwater vulnerability maps obtained through statistical methods A. Sorichetta et al.
437. Deep spatio-temporal learning for multi-hazard events: A ConvGRU multi-label classification approach S. Zahra et al.
438. Applications of geographical information systems and remote sensing in natural disaster hazard assessment and mitigation in Taiwan H. Merrett & W. Chen
439. Accounting for covariate distributions in slope-unit-based landslide susceptibility models. A case study in the alpine environment G. Amato et al.
440. Shaping shallow landslide susceptibility as a function of rainfall events M. Fumagalli et al.
441. Geospatial-based risk analysis of solar plants located in the mountainous region of Gangwon Province, South Korea M. Adhikari et al.
442. Rock mass geomechanical properties to improve rockfall susceptibility assessment: a case study in Valchiavenna (SO) G. Bajni et al.
443. Modelling landslide hazards under global changes: the case of a Pyrenean valley S. Bernardie et al.
444. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model H. Hussin et al.
445. Spatial Heterogeneity Modeling Using Machine Learning Based on a Hybrid of Random Forest and Convolutional Neural Network (CNN) A. Barry et al.
446. Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China X. Shao et al.
447. Determining Pleiades satellite data capability for tree diversity modeling H. Akbari & S. Kalbi
448. A new method of pseudo absence data generation in landslide susceptibility mapping with a case study of Shenzhen C. Xiao et al.
449. Shifting from traditional landslide occurrence modeling to scenario estimation with a “glass-box” machine learning F. Caleca et al.
450. A Survey of Internet of Things (IoT) for Geohazard Prevention: Applications, Technologies, and Challenges G. Mei et al.
451. Development of a landslide susceptibility assessment for a rail network K. Martinović et al.
452. Landslide Susceptibility Assessment in Zunyi City Incorporating MT-InSAR-Based Physical Constraints and Explainable Analysis Z. Zhang et al.
453. Fine Identification of Landslide Acceleration Phase Using Time Logarithm Prediction Method Based on Arc Synthetic Aperture Radar Monitoring Data C. Li et al.
454. Machine Learning for Gully Feature Extraction Based on a Pan-Sharpened Multispectral Image: Multiclass vs. Binary Approach K. Phinzi et al.
455. Mapping Post-Earthquake Landslide Susceptibility: A U-Net Like Approach Y. Chen et al.
456. Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion Y. Garosi et al.
457. Advancing Landslide Susceptibility Mapping in the Medea Region Using a Hybrid Metaheuristic ANFIS Approach F. Debiche et al.
458. Landslide Detection Using Densely Connected Convolutional Networks and Environmental Conditions H. Cai et al.
459. Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping B. Feizizadeh et al.
460. Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping A. Zhu et al.
461. Applicability of spatial prediction models for landslide susceptibility in land-use zoning instruments: a guideline in a coastal settlement in South-Central Chile P. López et al.
462. Landslide Susceptibility Estimation Using GIS. Evritania Prefecture: A Case Study in Greece G. Ntelis et al.
463. GIS-based spatial prediction of landslide susceptibility using logistic regression model S. Mousavi et al.
464. Prediction model for cyanide soil pollution in artisanal gold mining area by using logistic regression L. Razanamahandry et al.
465. How does forest structure affect root reinforcement and susceptibility to shallow landslides? C. Moos et al.
466. Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization X. Zhou et al.
467. Application of two fuzzy models using knowledge-based and linear aggregation approaches to identifying flooding-prone areas in Tehran M. Rezaei et al.
468. High-resolution earthquake-induced landslide hazard assessment in Southwest China through frequency ratio analysis and LightGBM Y. Wang et al.
469. High Resolution Precipitation and Soil Moisture Data Integration for Landslide Susceptibility Mapping Y. Peiro et al.
470. Neural network-based model for landslide susceptibility and soil longitudinal profile analyses: Two case studies F. Farrokhzad et al.
471. Effects of sampling intensity and non-slide/slide sample ratio on the occurrence probability of coseismic landslides X. Shao et al.
472. Multivariate geostatistical modeling of the spatial sediment distribution in a large scale drainage basin, Upper Rhone, Switzerland A. Schoch et al.
473. Improving the Generalization Performance of Debris-Flow Susceptibility Modeling by a Stacking Ensemble Learning-Based Negative Sample Strategy J. Li et al.
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483. Investigations of a Weathered and Closely Jointed Rock Slope Failure Using Back Analyses K. Shao et al.
484. A Novel Hybrid Machine Learning-Based Model for Rockfall Source Identification in Presence of Other Landslide Types Using LiDAR and GIS A. Fanos & B. Pradhan
485. Damage Assessment of Earthen Sites of the Ming Great Wall in Qinghai Province Y. Du et al.
486. Characteristics of landslides and assessment of deep-seated landslide susceptibility in Northern Turkey T. Duman & T. Çan
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488. Multivariate statistical algorithms for landslide susceptibility assessment in Kailash Sacred landscape, Western Himalaya A. Pandey et al.
489. GIS-based earthquake-triggered-landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China Y. Yi et al.
490. Prediction of Landslide Displacement by the Novel Coupling Method of HP Filtering Method and Extreme Gradient Boosting L. Zhou et al.
491. Comparative Study of Rapid Assessment Methods for Earthquake-Triggered Landslides Based on the Newmark Model—A Case Study of the 2022 Luding Ms6.8 Earthquake H. Li et al.
492. Comprehensive eco-geo-environmental assessment of the Sichuan–Yunnan ecological barrier zone using a random forest model X. Yang et al.
493. Random forest and spatial cross-validation performance in predicting species abundance distributions C. Mushagalusa et al.
494. Landslide Susceptibility Assessment Using Conditional Analysis and Rare Events Logistics Regression: A Case-Study in the Antrodoco Area (Rieti, Italy) V. Chiessi et al.