444 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. 10.5194/nhess-15-45-2015
2. Landslide susceptibility mapping using Bayesian approach in the Sultan Mountains (Akşehir, Turkey) A. Ozdemir 10.1007/s11069-011-9853-1
3. A refinement analysis of the shallow landslides susceptibility at regional scale supported by GIS-aided geo-database G. Gullà et al. 10.1080/19475705.2021.1967204
4. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling J. Goetz et al. 10.1016/j.cageo.2015.04.007
5. Validation and evaluation of two multivariate statistical models for predictive shallow landslide susceptibility mapping of the Eastern Pyrenees (Spain) C. Baeza et al. 10.1007/s12665-009-0361-5
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. 10.1016/S1001-6279(08)60010-4
7. Landslide susceptibility assessment in the Peloritani Mts. (Sicily, Italy) and clues for tectonic control of relief processes G. De Guidi & S. Scudero 10.5194/nhess-13-949-2013
8. 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
9. Rockfall susceptibility mapping using XGBoost model by hybrid optimized factor screening and hyperparameter H. Wen et al. 10.1080/10106049.2022.2120547
10. A spatial–temporal analysis of impacts from human development on the Shih-men Reservoir watershed, Taiwan Y. Chen & K. Chang 10.1080/01431161.2011.562253
11. Landslide Susceptibility Assessment of Heraklion Prefecture in Crete, Greece S. Adam et al. 10.12681/bgsg.28703
12. Scientometric Analysis for Spatial Autocorrelation-Related Research from 1991 to 2021 Q. Luo et al. 10.3390/ijgi11050309
13. Rainfall-Induced Landslides and Erosion Processes in the Road Network of the Jaén Province (Southern Spain) R. Carpena et al. 10.3390/hydrology8030100
14. Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand N. Intarawichian & S. Dasananda 10.1007/s12665-011-1055-3
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. 10.3390/s22166153
16. Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy C. Ballabio & S. Sterlacchini 10.1007/s11004-011-9379-9
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 10.1080/15230406.2024.2422593
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. 10.1007/s10064-019-01548-5
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. 10.5194/nhess-19-2207-2019
20. Landslide susceptibility mapping based on global and local logistic regression models in Three Gorges Reservoir area, China M. Zhang et al. 10.1007/s12665-016-5764-5
21. Spatial analysis of groundwater flowing artesian condition using machine learning techniques A. Al-Abadi & J. Alsamaani 10.1016/j.gsd.2020.100418
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. 10.1016/j.geomorph.2009.09.023
23. An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method S. Shrestha et al. 10.3390/ijgi6110365
24. Estimating the prediction performance of spatial models via spatial k-fold cross validation J. Pohjankukka et al. 10.1080/13658816.2017.1346255
25. From scenario-based seismic hazard to scenario-based landslide hazard: fast-forwarding to the future via statistical simulations L. Lombardo & H. Tanyas 10.1007/s00477-021-02020-1
26. Entropy Ensemble Filter: A Modified Bootstrap Aggregating (Bagging) Procedure to Improve Efficiency in Ensemble Model Simulation H. Foroozand & S. Weijs 10.3390/e19100520
27. GIS-based analysis of landslides susceptibility mapping: a case study of Lushoto district, north-eastern Tanzania M. Makonyo & Z. Zahor 10.1007/s11069-023-06038-2
28. Evaluating performance of simplified physically based models for shallow landslide susceptibility G. Formetta et al. 10.5194/hess-20-4585-2016
29. 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. 10.4236/ijg.2017.84031
30. Ensemble classification of paired data W. Adler et al. 10.1016/j.csda.2010.11.017
31. Tier 4 maps of soil pH at 25 m resolution for the Netherlands A. Helfenstein et al. 10.1016/j.geoderma.2021.115659
32. 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 10.5194/hess-10-663-2006
33. Retrogressive thaw slump susceptibility in the northern hemisphere permafrost region E. Makopoulou et al. 10.1002/esp.5890
34. Fourth paradigm GIScience? Prospects for automated discovery and explanation from data M. Gahegan 10.1080/13658816.2019.1652304
35. Different Approaches to Use Morphometric Attributes in Landslide Susceptibility Mapping Based on Meso-Scale Spatial Units: A Case Study in Rio de Janeiro (Brazil) V. Canavesi et al. 10.3390/rs12111826
36. A Comparative Assessment of Machine Learning Models for Landslide Susceptibility Mapping in the Rugged Terrain of Northern Pakistan N. Shahzad et al. 10.3390/app12052280
37. A review of landslides related to the 2005 Kashmir Earthquake: implication and future challenges M. Basharat et al. 10.1007/s11069-021-04688-8
38. Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China W. Chen et al. 10.1007/s12665-015-5093-0
39. Spatial cross-validation is not the right way to evaluate map accuracy A. Wadoux et al. 10.1016/j.ecolmodel.2021.109692
40. 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
41. An evaluation of SVM using polygon-based random sampling in landslide susceptibility mapping: The Candir catchment area (western Antalya, Turkey) B. San 10.1016/j.jag.2013.09.010
42. 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. 10.5194/nhess-14-2449-2014
43. Landslide susceptibility mapping using physics-guided machine learning: a case study of a debris flow event in Colorado Front Range T. Pei & T. Qiu 10.1007/s11440-024-02384-y
44. Effects of rainfall spatial variability and intermittency on shallow landslide triggering patterns at a catchment scale J. von Ruette et al. 10.1002/2013WR015122
45. Supervised classification of landforms in Arctic mountains H. Mithan et al. 10.1002/ppp.2015
46. Landslide susceptibility mapping using deep learning models in Ardabil province, Iran H. Hamedi et al. 10.1007/s00477-022-02263-6
47. 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. 10.1007/s12665-015-4028-0
48. 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. 10.1016/j.geomorph.2020.107307
49. Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models R. Schlögel et al. 10.1016/j.geomorph.2017.10.018
50. Space-time landslide predictive modelling L. Lombardo et al. 10.1016/j.earscirev.2020.103318
51. Landslide Analysis Approaches in Tropical Environment Region for Disaster Risk Reduction M. Kahar et al. 10.1088/1755-1315/1102/1/012025
52. Satellite-Based NO2 and Model Validation in a National Prediction Model Based on Universal Kriging and Land-Use Regression M. Young et al. 10.1021/acs.est.5b05099
53. Simulation of Forest Distribution in the Qilian Mountains of China with a Terrain-based Logistic Regression Model S. Fang et al. 10.1093/forsci/fxac040
54. Predicting gully occurrence at watershed scale: Comparing topographic indices and multivariate statistical models C. Conoscenti & E. Rotigliano 10.1016/j.geomorph.2020.107123
55. Effects of raster resolution on real probability of landslides X. Shao et al. 10.1016/j.rsase.2020.100364
56. Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa J. Nsengiyumva et al. 10.1016/j.scitotenv.2018.12.248
57. 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. 10.1007/s11069-012-0347-6
58. A topography‐based model of forest cover at the alpine tree line in the tropical Andes M. Bader & J. Ruijten 10.1111/j.1365-2699.2007.01818.x
59. Landslide susceptibility mapping with GIS in high mountain area of Nepal: a comparison of four methods P. Gautam et al. 10.1007/s12665-021-09650-2
60. 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
61. Catchment natural driving factors and prediction of baseflow index for Continental United States based on Random Forest technique S. Huang et al. 10.1007/s00477-021-02057-2
62. Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine C. Su et al. 10.1007/s11069-014-1562-0
63. 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. 10.1007/s10064-015-0786-x
64. Landslide susceptibility assessment in scarce-data regions using remote sensing data E. Aristizábal-Giraldo & D. Ruiz-Vásquez 10.17533/udea.redin.20231030
65. A random forests-based hedonic price model accounting for spatial autocorrelation E. Tepe 10.1007/s10109-024-00449-w
66. Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping Y. Wu et al. 10.1016/j.catena.2019.104396
67. Impact of Spatial Resolution of Digital Elevation Model on Landslide Susceptibility Mapping: A Case Study in Kullu Valley, Himalayas S. Meena & T. Gudiyangada Nachappa 10.3390/geosciences9080360
68. Improvement of landslide hazard assessments for regulatory zoning in France: STATE–OF–THE-ART perspectives and considerations Y. Thiery et al. 10.1016/j.ijdrr.2020.101562
69. Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey M. Loche et al. 10.3390/rs14061321
70. Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models A. Arora et al. 10.1080/10106049.2019.1687594
71. Spatial analysis of ecological risk in a coastal dune landscape using high resolution aerial photography N. ListyaNiNgrum et al. 10.14746/quageo-2024-0001
72. Random Forest with semantic tie points for classifying landforms and creating rigorous shaded relief representations F. Veronesi & L. Hurni 10.1016/j.geomorph.2014.07.020
73. 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. 10.1007/s12665-022-10225-y
74. 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. 10.1007/s10346-015-0614-1
75. Combining release and runout in statistical landslide susceptibility modeling M. Mergili et al. 10.1007/s10346-019-01222-7
76. Spatial and temporal variability of soil saturated hydraulic conductivity in gradients of disturbance B. Zimmermann & H. Elsenbeer 10.1016/j.jhydrol.2008.07.027
77. Source areas, connectivity, and delivery rate of sediments in mountainous-forested hillslopes: A probabilistic approach A. Cislaghi & G. Bischetti 10.1016/j.scitotenv.2018.10.318
78. Do landslides follow landslides? Insights in path dependency from a multi-temporal landslide inventory J. Samia et al. 10.1007/s10346-016-0739-x
79. Regional-scale landslide risk assessment in Central Asia F. Caleca et al. 10.5194/nhess-24-13-2024
80. Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows T. Heckmann et al. 10.5194/nhess-14-259-2014
81. Data-driven mapping of the potential mountain permafrost distribution N. Deluigi et al. 10.1016/j.scitotenv.2017.02.041
82. 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. 10.1007/s11069-018-3536-0
83. How can statistical models help to determine driving factors of landslides? P. Vorpahl et al. 10.1016/j.ecolmodel.2011.12.007
84. Flow-Py v1.0: a customizable, open-source simulation tool to estimate runout and intensity of gravitational mass flows C. D'Amboise et al. 10.5194/gmd-15-2423-2022
85. 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. 10.1007/s12517-015-1937-x
86. Spatial Non-Stationarity-Based Landslide Susceptibility Assessment Using PCAMGWR Model Y. Li et al. 10.3390/w14060881
87. 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. 10.1007/s10346-012-0320-1
88. 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. 10.1080/19475705.2013.843206
89. Effects of sample size on the accuracy of geomorphological models J. Hjort & M. Marmion 10.1016/j.geomorph.2008.04.006
90. Landslide susceptibility evaluation and hazard zonation techniques – a review L. Shano et al. 10.1186/s40677-020-00152-0
91. 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. 10.1080/22797254.2019.1596756
92. Soil erodibility assessment of laterite dominant sub-basin watersheds in the humid tropical region of India A. Gayen & S. Mafizul Haque 10.1016/j.catena.2022.106161
93. Effect of Earthquake on Subsequent Typhoon-Induced Landslides Using Remote Sensing Imagery in the 99 Peaks Region, Central Taiwan S. Lee et al. 10.4028/www.scientific.net/KEM.500.773
94. Multi-Temporal Landslide Inventory-Based Statistical Susceptibility Modeling Associated With the 2017 Mw 6.5 Jiuzhaigou Earthquake, Sichuan, China L. Luo et al. 10.3389/fenvs.2022.858635
95. A Tool for Classification and Regression Using Random Forest Methodology: Applications to Landslide Susceptibility Mapping and Soil Thickness Modeling D. Lagomarsino et al. 10.1007/s10666-016-9538-y
96. A Comparative Analysis of Machine Learning Techniques for National Glacier Mapping: Evaluating Performance through Spatial Cross-Validation in Perú M. Bueno et al. 10.3390/w15244214
97. Predicting Soil Erodibility Using Electrical Resistivity Tomography M. Karim & S. Tucker-Kulesza 10.1061/(ASCE)GT.1943-5606.0001857
98. A hybrid random forests and artificial neural networks bagging ensemble for landslide susceptibility modelling L. Lucchese et al. 10.1080/10106049.2022.2109761
99. Predicting species abundance using machine learning approach: a comparative assessment of random forest spatial variants and performance metrics C. Mushagalusa et al. 10.1007/s40808-024-02055-7
100. Estimation of erosion in tectonically active orogenies. Example from the Bhotekoshi catchment, Himalaya (Nepal) C. Andermann & R. Gloaguen 10.1080/01431160802558733
101. Evaluation of Machine Learning Performance in Wildfire Susceptibility Mapping Under Limited Training Data Condition M. SHAFAPOURTEHRANY 10.46387/bjesr.1174006
102. 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. 10.1016/j.ecolind.2020.106889
103. Application of an evidential belief function model in landslide susceptibility mapping O. Althuwaynee et al. 10.1016/j.cageo.2012.03.003
104. Shallow landslides susceptibility assessment in different environments M. Persichillo et al. 10.1080/19475705.2016.1265011
105. A functional entity approach to predict soil erosion processes in a small Plio-Pleistocene Mediterranean catchment in Northern Chianti, Italy M. Märker et al. 10.1016/j.geomorph.2010.10.022
106. Landslide hazard mapping using GIS and weight of evidence model in Qingshui River watershed of 2008 Wenchuan earthquake struck region C. Xu et al. 10.1007/s12583-012-0236-7
107. Multifactor empirical mapping of the protective function of forests against landslide occurrence: statistical approaches and a case study D. Cimini et al. 10.3832/ifor1740-008
108. Effect of landslide spatial representation and raster resolution on the landslide susceptibility assessment S. Yang et al. 10.1007/s12665-024-11442-3
109. 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
110. A novel voting ensemble model for spatial prediction of landslides using GIS S. Sachdeva et al. 10.1080/01431161.2019.1654141
111. Forest cover change trajectories and their impact on landslide occurrence in the tropical Andes M. Guns & V. Vanacker 10.1007/s12665-013-2352-9
112. A free web-based approach for rainfall-induced landslide susceptibility modeling: Case study of Clearwater National Forest, Idaho, USA P. Gorsevski 10.1016/j.envsoft.2023.105632
113. 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. 10.3390/rs14153620
114. 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. 10.3390/rs11080978
115. A step beyond landslide susceptibility maps: a simple method to investigate and explain the different outcomes obtained by different approaches T. Xiao et al. 10.1007/s10346-019-01299-0
116. Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey H. Akinci & M. Zeybek 10.1007/s11069-021-04743-4
117. The propagation of inventory-based positional errors into statistical landslide susceptibility models S. Steger et al. 10.5194/nhess-16-2729-2016
118. Evaluation of predictive models for post-fire debris flow occurrence in the western United States E. Nikolopoulos et al. 10.5194/nhess-18-2331-2018
119. 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. 10.1016/j.geomorph.2012.04.023
120. 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. 10.1016/j.catena.2012.04.001
121. Kernel-Based Mapping of Orographic Rainfall Enhancement in the Swiss Alps as Detected by Weather Radar L. Foresti et al. 10.1109/TGRS.2011.2179550
122. Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction H. Meyer et al. 10.1016/j.ecolmodel.2019.108815
123. iSoLIM : a similarity-based spatial prediction software for the big data era F. Zhao et al. 10.1080/19475683.2024.2324381
124. Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy) D. Costanzo et al. 10.1007/s10346-013-0415-3
125. Multi-Resolution Landslide Susceptibility Analysis Using a DEM and Random Forest U. Paudel et al. 10.4236/ijg.2016.75056
126. Random Forest-Based Landslide Susceptibility Mapping in Coastal Regions of Artvin, Turkey H. Akinci et al. 10.3390/ijgi9090553
127. Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution A. Shirzadi et al. 10.1016/j.catena.2019.03.017
128. Big Data Analytics for Emergency Communication Networks: A Survey J. Wang et al. 10.1109/COMST.2016.2540004
129. 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. 10.1016/j.catena.2019.104150
130. Combination of Rainfall Thresholds and Susceptibility Maps for Dynamic Landslide Hazard Assessment at Regional Scale S. Segoni et al. 10.3389/feart.2018.00085
131. 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. 10.1016/j.enggeo.2007.11.011
132. 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. 10.1016/j.geomorph.2014.09.020
133. A grid portal for solving geoscience problems using distributed knowledge discovery services G. Folino et al. 10.1016/j.future.2009.08.002
134. 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. 10.1016/j.scitotenv.2020.140549
135. Landslide susceptibility: a statistically-based assessment on a depositional pyroclastic ramp F. Murillo-García et al. 10.1007/s11629-018-5225-6
136. Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting A. van Natijne et al. 10.3390/s20051425
137. 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. 10.3390/rs11131589
138. Regional-scale controls on the spatial activity of rockfalls (Turtmann Valley, Swiss Alps) — A multivariate modeling approach K. Messenzehl et al. 10.1016/j.geomorph.2016.01.008
139. Integration of rainfall thresholds and susceptibility maps in the Emilia Romagna (Italy) regional-scale landslide warning system S. Segoni et al. 10.1007/s10346-014-0502-0
140. Broad-scale species distribution models applied to data-poor areas C. Guillaumot et al. 10.1016/j.pocean.2019.04.007
141. A Physically Constrained Variational Autoencoder for Geochemical Pattern Recognition Y. Xiong et al. 10.1007/s11004-021-09979-1
142. Lethality level analysis of secondary landslides based on field survey data: a case study of Luding earthquake L. Yao et al. 10.1007/s10346-023-02172-x
143. Landslide Susceptibility Assessment in Nepal’s Chure Region: A Geospatial Analysis P. Thapa et al. 10.3390/land12122186
144. Landslide susceptibility mapping at sin Ho, Lai Chau province, Vietnam using ensemble models based on fuzzy unordered rules induction algorithm T. Bien et al. 10.1080/10106049.2022.2136253
145. PSLSA v2.0: An automatic Python package integrating machine learning models for regional landslide susceptibility assessment Z. Guo et al. 10.1016/j.envsoft.2025.106367
146. 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 10.1007/BF03013495
147. Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy S. Segoni et al. 10.3390/rs16234491
148. 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
149. A review on spatial, temporal and magnitude prediction of landslide hazard A. Tyagi et al. 10.1016/j.jaesx.2022.100099
150. Spatial prediction of spring locations in data poor region of Central Himalayas R. Niraula et al. 10.2166/nh.2020.223
151. Optimizing and validating the Gravitational Process Path model for regional debris-flow runout modelling J. Goetz et al. 10.5194/nhess-21-2543-2021
152. 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. 10.1016/j.scitotenv.2019.01.221
153. Comparing methods of landslide data acquisition and susceptibility modelling: Examples from New Zealand H. Smith et al. 10.1016/j.geomorph.2021.107660
154. A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping A. Zhu et al. 10.1016/j.catena.2018.04.003
155. Spatial statistical modeling of shallow landslides—Validating predictions for different landslide inventories and rainfall events J. von Ruette et al. 10.1016/j.geomorph.2011.06.010
156. Landslide Susceptibility Mapping Using Machine Learning: A Danish Case Study A. Ageenko et al. 10.3390/ijgi11060324
157. 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. 10.5194/nhess-24-2615-2024
158. Assessment of rainfall-induced landslide susceptibility in Artvin, Turkey using machine learning techniques H. Akinci 10.1016/j.jafrearsci.2022.104535
159. The Effect of Observation Data Sampling Methods on Infiltration Areas by Maximum Entropy Model M. Jafarzadeh et al. 10.52547/jwmr.11.22.96
160. Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility P. Lima et al. 10.1007/s11629-021-7254-9
161. Field Measurements of Passive Earth Forces in Steep, Shallow, Landslide‐Prone Areas A. Cislaghi et al. 10.1029/2017JF004557
162. Review on landslide susceptibility mapping using support vector machines Y. Huang & L. Zhao 10.1016/j.catena.2018.03.003
163. Comprehensive landslide susceptibility map of Central Asia A. Rosi et al. 10.5194/nhess-23-2229-2023
164. Uncertainties of landslide susceptibility prediction considering different landslide types F. Huang et al. 10.1016/j.jrmge.2023.03.001
165. Application of a Novel Hybrid Machine Learning Algorithm in Shallow Landslide Susceptibility Mapping in a Mountainous Area B. Ghasemian et al. 10.3389/fenvs.2022.897254
166. Building a landslide hazard indicator with machine learning and land surface models T. Stanley et al. 10.1016/j.envsoft.2020.104692
167. Active-Learning Approaches for Landslide Mapping Using Support Vector Machines Z. Wang & A. Brenning 10.3390/rs13132588
168. 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. 10.1016/j.geomorph.2014.12.042
169. Catchment-scale impacts of shallow landslides on stream water chemistry N. Yoshihara et al. 10.1016/j.scitotenv.2022.153970
170. Landslide susceptibility mapping on a global scale using the method of logistic regression L. Lin et al. 10.5194/nhess-17-1411-2017
171. Quantifying effectiveness of trees for landslide erosion control R. Spiekermann et al. 10.1016/j.geomorph.2021.107993
172. Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results V. Capecchi et al. 10.5194/nhess-15-75-2015
173. Multi-scale support vector algorithms for hot spot detection and modelling A. Pozdnoukhov & M. Kanevski 10.1007/s00477-007-0162-x
174. Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling J. Lai et al. 10.3390/ijgi8090397
175. Assessing Landslide Drivers in Social–Ecological–Technological Systems: The Case of Metropolitan Region of São Paulo, Brazil M. Hirye et al. 10.3390/rs15123048
176. Transferability of geomorphological distribution models: Evaluation using solifluction features in subarctic and Arctic regions J. Hjort et al. 10.1016/j.geomorph.2013.08.002
177. How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? — A catchment-scale case study from China Z. Guo et al. 10.1016/j.jrmge.2023.07.026
178. 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. 10.3390/s19224893
179. Exploring performance and robustness of shallow landslide susceptibility modeling at regional scale using different training and testing sets M. Conforti et al. 10.1007/s12665-023-10844-z
180. Developing a more complete understanding of tropical montane forest disturbance ecology through landslide research C. Freund & M. Silman 10.3389/ffgc.2023.1091387
181. Landslide susceptibility assessment in complex geological settings: sensitivity to geological information and insights on its parameterization S. Segoni et al. 10.1007/s10346-019-01340-2
182. 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. 10.1016/j.catena.2016.03.028
183. 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 10.1007/s11069-018-3548-9
184. Coğrafi Bilgi Sistemleri ile Suşehri (Sivas) Heyelan Duyarlılık Analizi G. DEMİR 10.17714/gumusfenbil.299987
185. On the use of adversarial validation for quantifying dissimilarity in geospatial machine learning prediction Y. Wang et al. 10.1080/15481603.2025.2460513
186. Hybrid meta-heuristic machine learning methods applied to landslide susceptibility mapping in the Sahel-Algiers M. Benbouras 10.1016/j.ijsrc.2022.04.003
187. Engineered slope failure susceptibility modelling using high spatial resolution geospatial data S. Obrike et al. 10.1007/s10064-021-02413-0
188. When Enough Is Really Enough? On the Minimum Number of Landslides to Build Reliable Susceptibility Models G. Titti et al. 10.3390/geosciences11110469
189. 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. 10.1007/s12524-009-0028-4
190. 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. 10.1007/s11069-013-0661-7
191. Joint modelling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions R. Yadav et al. 10.1093/jrsssc/qlad077
192. The performance of landslide susceptibility models critically depends on the quality of digital elevation models J. Brock et al. 10.1080/19475705.2020.1776403
193. 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
194. Spatial+: A new cross-validation method to evaluate geospatial machine learning models Y. Wang et al. 10.1016/j.jag.2023.103364
195. A review of regularised estimation methods and cross-validation in spatiotemporal statistics P. Otto et al. 10.1214/24-SS150
196. Fiber bundle model for multiscale modeling of hydromechanical triggering of shallow landslides D. Cohen et al. 10.1029/2009WR007889
197. Evaluation of Different Landslide Susceptibility Models for a Local Scale in the Chitral District, Northern Pakistan B. Aslam et al. 10.3390/s22093107
198. How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Y. Achour & H. Pourghasemi 10.1016/j.gsf.2019.10.001
199. Disaggregating County-Level Census Data for Population Mapping Using Residential Geo-Objects With Multisource Geo-Spatial Data T. Wu et al. 10.1109/JSTARS.2020.2974896
200. 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. 10.1016/j.jhydrol.2021.127047
201. Landslide failure and runout susceptibility in the upper T. Ceno valley (Northern Apennines, Italy) A. Clerici et al. 10.1007/s11069-009-9349-4
202. Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River Basin (Sicily, Italy) S. Angileri et al. 10.1016/j.geomorph.2016.03.018
203. 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 10.1007/s12594-012-0171-3
204. Swarm optimization based heterogeneous machine learning techniques for enhanced landslide susceptibility assessment with comprehensive uncertainty quantification S. Dey & S. Das 10.1007/s12145-024-01617-8
205. Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India D. Kumar et al. 10.1016/j.geomorph.2017.06.013
206. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS B. Pradhan 10.1016/j.cageo.2012.08.023
207. 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. 10.1007/s12517-018-3531-5
208. A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models D. Davoudi Moghaddam et al. 10.3390/w12030679
209. Prediction of the landslide susceptibility: Which algorithm, which precision? H. Pourghasemi & O. Rahmati 10.1016/j.catena.2017.11.022
210. GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China Y. Zhao et al. 10.1016/j.enggeo.2019.105147
211. GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China C. Xu et al. 10.1016/j.geomorph.2011.12.040
212. 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. 10.3389/feart.2021.781472
213. Landslide occurrence and its relation with terrain factors in the Siwalik Hills, Nepal: case study of susceptibility assessment in three basins M. Ghimire 10.1007/s11069-010-9569-7
214. Landslide susceptibility assessment using SVM machine learning algorithm M. Marjanović et al. 10.1016/j.enggeo.2011.09.006
215. Modeling the spatial distribution of subarctic forest in northern Manitoba using GIS-based terrain and climate data S. Mamet et al. 10.1080/02723646.2014.994253
216. A methodological approach of QRA for slow-moving landslides at a regional scale F. Caleca et al. 10.1007/s10346-022-01875-x
217. 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. 10.3390/rs15051376
218. Analytical techniques for mapping multi-hazard with geo-environmental modeling approaches and UAV images N. Kariminejad et al. 10.1038/s41598-022-18757-w
219. 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
220. Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization K. Khosravi et al. 10.5194/hess-22-4771-2018
221. Métodos estadísticos para la evaluación de la susceptibilidad por movimientos en masa E. Aristizábal-Giraldo et al. 10.22430/22565337.1247
222. 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
223. 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
224. A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods A. Zhu et al. 10.1016/j.catena.2019.104188
225. Classification Efficacy Using K-Fold Cross-Validation and Bootstrapping Resampling Techniques on the Example of Mapping Complex Gully Systems K. Phinzi et al. 10.3390/rs13152980
226. Integrating geomorphology, statistic and numerical simulations for landslide invasion hazard scenarios mapping: An example in the Sorrento Peninsula (Italy) F. Lucà et al. 10.1016/j.cageo.2014.01.006
227. Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression I. Colkesen et al. 10.1016/j.jafrearsci.2016.02.019
228. 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
229. 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 10.5194/adgeo-22-95-2009
230. An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm A. Akgun et al. 10.1016/j.cageo.2011.04.012
231. On the use of explainable AI for susceptibility modeling: Examining the spatial pattern of SHAP values N. Wang et al. 10.1016/j.gsf.2024.101800
232. 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. 10.3390/rs15194733
233. GIS-based bivariate statistical modelling for earthquake-triggered landslides susceptibility mapping related to the 2008 Wenchuan earthquake, China C. Xu et al. 10.1144/qjegh2012-006
234. An introduction to learning algorithms and potential applications in geomorphometry and Earth surface dynamics A. Valentine & L. Kalnins 10.5194/esurf-4-445-2016
235. Improving sinkhole hazard models incorporating magnitude–frequency relationships and nearest neighbor analysis J. Galve et al. 10.1016/j.geomorph.2011.05.020
236. Identifying the controls on coastal cliff landslides using machine-learning approaches M. Dickson & G. Perry 10.1016/j.envsoft.2015.10.029
237. Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system M. Ahmed et al. 10.1016/j.jag.2023.103593
238. Integrating predictive modeling techniques with geospatial data for landslide susceptibility assessment in northern Pakistan A. Asghar et al. 10.1007/s11629-023-8029-2
239. Landslide susceptibility assessment using the certainty factor and deep neural network W. Ma et al. 10.3389/feart.2022.1091560
240. Spatial machine-learning model diagnostics: a model-agnostic distance-based approach A. Brenning 10.1080/13658816.2022.2131789
241. Spatial prediction strategy for landslides triggered by large earthquakes oriented to emergency response, mid-term resettlement and later reconstruction S. Ma et al. 10.1016/j.ijdrr.2019.101362
242. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression T. Kavzoglu et al. 10.1007/s10346-013-0391-7
243. Improved environmental mapping and validation using bagging models with spatially clustered data B. Misiuk & C. Brown 10.1016/j.ecoinf.2023.102181
244. Electrical resistivity as a binary classifier for bridge scour evaluation M. Karim et al. 10.1016/j.trgeo.2019.03.002
245. 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. 10.1002/2013WR014560
246. Spatial statistics and soil mapping: A blossoming partnership under pressure G. Heuvelink & R. Webster 10.1016/j.spasta.2022.100639
247. Indirect modeling of hourly meteorological time series for winter road maintenance A. Brenning et al. 10.1002/env.1072
248. Assessment and validation of GIS-based landslide susceptibility maps: a case study from Feltrino stream basin (Central Italy) M. Sciarra et al. 10.1007/s10064-016-0954-7
249. The 2010 Yushu earthquake triggered landslide hazard mapping using GIS and weight of evidence modeling C. Xu et al. 10.1007/s12665-012-1624-0
250. Seismically induced landslide hazard and exposure modelling in Southern California based on the 1994 Northridge, California earthquake event M. Budimir et al. 10.1007/s10346-014-0531-8
251. Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression M. García-Rodríguez et al. 10.1016/j.geomorph.2007.06.001
252. Spatial leave‐one‐out cross‐validation for variable selection in the presence of spatial autocorrelation K. Le Rest et al. 10.1111/geb.12161
253. 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. 10.1007/s12665-017-6640-7
254. 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. 10.1007/s10346-015-0557-6
255. Comparisons of filter, wrapper, and embedded feature selection for rockfall susceptibility prediction and mapping C. Lei et al. 10.1007/s11069-024-06878-6
256. Benchmarking data handling strategies for landslide susceptibility modeling using random forest workflows G. Samodra et al. 10.1016/j.aiig.2024.100093
257. Mapping flood susceptibility in an arid region of southern Iraq using ensemble machine learning classifiers: a comparative study A. Al-Abadi 10.1007/s12517-018-3584-5
258. 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. 10.1007/s12518-024-00548-9
259. A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS T. Chen et al. 10.1007/s12665-016-5317-y
260. Macroscopic Lane Change Model—A Flexible Event-Tree-Based Approach for the Prediction of Lane Change on Freeway Traffic C. Ng et al. 10.3390/smartcities4020044
261. 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
262. The use of digital technologies for landslide disaster risk research and disaster risk management: progress and prospects H. Bao et al. 10.1007/s12665-022-10575-7
263. Three oversampling methods applied in a comparative landslide spatial research in Penang Island, Malaysia H. Gao et al. 10.1007/s42452-020-03307-8
264. 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
265. Development of a morphometric connectivity model to mitigate sediment derived from storm-driven shallow landslides R. Spiekermann et al. 10.1016/j.ecoleng.2022.106676
266. A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China W. Xie et al. 10.3390/ijgi10020093
267. Spatial Regression Models for Field Trials: A Comparative Study and New Ideas S. Hawinkel et al. 10.3389/fpls.2022.858711
268. 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. 10.1186/s40562-022-00249-4
269. Comparative analysis of certainty factor and analytic hierarchy process for landslide susceptibility zonation in parts of Solan, Himachal Pradesh, India A. Magray et al. 10.14746/quageo-2023-0020
270. Incorporation of neighborhood information improves performance of SDB models A. Knudby & G. Richardson 10.1016/j.rsase.2023.101033
271. Landslide susceptibility mapping by using a geographic information system (GIS) along the China–Pakistan Economic Corridor (Karakoram Highway), Pakistan S. Ali et al. 10.5194/nhess-19-999-2019
272. 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. 10.1007/s10346-024-02287-9
273. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine I. Yilmaz 10.1007/s12665-009-0394-9
274. A software framework for classification models of geographical data Y. Liu et al. 10.1016/j.cageo.2012.02.006
275. 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
276. Development of a binary logistic lane change model and its validation using empirical freeway data C. Ng et al. 10.1080/21680566.2020.1715309
277. Landslide susceptibility mapping using AHP and fuzzy methods in the Gilan province, Iran Y. Bahrami et al. 10.1007/s10708-020-10162-y
278. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran H. Pourghasemi et al. 10.1007/s11069-012-0217-2
279. Soft computing in assessment of earthquake-triggered landslide susceptibility C. Xu et al. 10.1007/s12665-016-5576-7
280. A hybrid machine learning model for landslide-oriented risk assessment of long-distance pipelines H. Wen et al. 10.1016/j.jenvman.2023.118177
281. Pre-processing algorithms and landslide modelling on remotely sensed DEMs M. Santini et al. 10.1016/j.geomorph.2009.03.023
282. Data-driven topo-climatic mapping with machine learning methods A. Pozdnoukhov et al. 10.1007/s11069-008-9339-y
283. 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. 10.1007/s11069-020-04169-4
284. 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
285. The RUSLE erosion index as a proxy indicator for debris flow susceptibility A. Zini et al. 10.1007/s10346-014-0515-8
286. Towards landslide space-time forecasting through machine learning: the influence of rainfall parameters and model setting N. Nocentini et al. 10.3389/feart.2023.1152130
287. Mapping Fractional Land Use and Land Cover in a Monsoon Region: The Effects of Data Processing Options B. Seo et al. 10.1109/JSTARS.2016.2544802
288. A quick method of early landslide identification based on dynamic susceptibility analysis using M-SVM method: a case study Y. Liu et al. 10.1007/s10064-023-03440-9
289. Comparison and combination of different models for optimal landslide susceptibility zonation G. Chen et al. 10.1144/qjegh2013-071
290. Susceptibility mapping of instability related to shallow mining cavities in a built-up environment R. Pellicani et al. 10.1016/j.enggeo.2016.12.011
291. 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. 10.1002/ldr.3151
292. Assessment of calanchi and rill-interrill erosion susceptibility in northern Liguria, Italy: A case study using a probabilistic modelling framework M. Maerker et al. 10.1016/j.geoderma.2020.114367
293. 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. 10.1007/s11069-015-1915-3
294. 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 10.1007/s10706-020-01284-8
295. A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery L. Xu et al. 10.1016/j.rse.2013.10.012
296. Evaluation of geological risk and hydrocarbon favorability using logistic regression model with case study Z. Zhu et al. 10.1016/j.marpetgeo.2018.02.012
297. Exploring the impact of introducing a physical model into statistical methods on the evaluation of regional scale debris flow susceptibility J. Sun et al. 10.1007/s11069-020-04498-4
298. Comparison of Statistical Approaches for Modelling Land-Use Change B. Sun & D. Robinson 10.3390/land7040144
299. Geomorphic process rates of landslides along a humidity gradient in the tropical Andes J. Muenchow et al. 10.1016/j.geomorph.2011.10.029
300. Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area Q. Su et al. 10.3390/ijgi6070228
301. Uncertainty due to DEM error in landslide susceptibility mapping C. Qin et al. 10.1080/13658816.2013.770515
302. Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping L. Lucchese et al. 10.1007/s11069-021-04547-6
303. Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression D. Bui et al. 10.1007/s11069-011-9844-2
304. Terrain Controls and Landscape-Scale Susceptibility Modelling of Active-Layer Detachments, Sabine Peninsula, Melville Island, Nunavut A. Rudy et al. 10.1002/ppp.1900
305. Benchmarking classifiers to optimally integrate terrain analysis and multispectral remote sensing in automatic rock glacier detection A. Brenning 10.1016/j.rse.2008.09.005
306. Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China C. Xu et al. 10.1016/j.cageo.2012.01.002
307. Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models D. Tien Bui et al. 10.1155/2012/974638
308. Distinctive controls on the distribution of river-damming and non-damming landslides induced by the 2008 Wenchuan earthquake R. Tang et al. 10.1007/s10064-018-1381-8
309. A systematic review of landslide probability mapping using logistic regression M. Budimir et al. 10.1007/s10346-014-0550-5
310. 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. 10.1007/s00704-015-1702-9
311. 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
312. Assessing the utility of regionalized rock-mass geomechanical properties in rockfall susceptibility modelling in an alpine environment G. Bajni et al. 10.1016/j.geomorph.2022.108401
313. Integrating physical and empirical landslide susceptibility models using generalized additive models J. Goetz et al. 10.1016/j.geomorph.2011.03.001
314. Landslide susceptibility of the Prato–Pistoia–Lucca provinces, Tuscany, Italy S. Segoni et al. 10.1080/17445647.2016.1233463
315. 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
316. Autologistic modelling of susceptibility to landsliding in the Central Apennines, Italy P. Atkinson & R. Massari 10.1016/j.geomorph.2011.02.001
317. Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria P. Lima et al. 10.1007/s10346-021-01693-7
318. 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. 10.1007/s12665-023-10846-x
319. 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. 10.1016/j.asr.2024.03.074
320. Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek, South Korea A. Pradhan & Y. Kim 10.1007/s11069-014-1065-z
321. An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic A. Zhu et al. 10.1016/j.geomorph.2014.02.003
322. A side-sampling based Linformer model for landslide susceptibility assessment: a case study of the railways in China N. Jiang et al. 10.1080/19475705.2024.2354507
323. Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data P. Schratz et al. 10.1016/j.ecolmodel.2019.06.002
324. An assessment on the use of Terra ASTER L3A data in landslide susceptibility mapping H. Nefeslioglu et al. 10.1016/j.jag.2011.08.005
325. 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
326. Dynamic association of slope movements in the Uttarakhand Himalaya: a critical review on the landslide susceptibility assessment H. Khali et al. 10.1080/19475705.2023.2273214
327. Soybean yield prediction using machine learning algorithms under a cover crop management system L. Santos et al. 10.1016/j.atech.2024.100442
328. Reasons for an outstanding plant diversity in the tropical Andes of Southern Ecuador M. Richter et al. 10.3097/LO.200912
329. 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. 10.1109/JSTARS.2014.2341276
330. 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
331. A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses B. Pradhan et al. 10.1016/j.compenvurbsys.2009.12.004
332. Scaling land-surface variables for landslide detection F. Sîrbu et al. 10.1186/s40645-019-0290-1
333. 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. 10.1088/1755-1315/1091/1/012010
334. 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 10.1186/s40677-019-0124-x
335. Quantitative analysis of landslide impact on vegetation: Insights from field surveys and UAV imagery S. Yousefi et al. 10.1016/j.ecoleng.2025.107581
336. Method for prediction of landslide movements based on random forests M. Krkač et al. 10.1007/s10346-016-0761-z
337. A remote sensing-based tool for assessing rainfall-driven hazards D. Wright et al. 10.1016/j.envsoft.2016.12.006
338. Landslide susceptibility mapping using precipitation data, Mazandaran Province, north of Iran M. Arab Amiri & C. Conoscenti 10.1007/s11069-017-2962-8
339. How can landslide risk maps be validated? Potential solutions with open-source databases F. Caleca et al. 10.3389/feart.2022.998885
340. Landslide susceptibility assessment of the Youfang catchment using logistic regression S. Bai et al. 10.1007/s11629-014-3171-5
341. Machine learning and multi-sensor based modelling of woody vegetation in the Molopo Area, South Africa M. Ludwig et al. 10.1016/j.rse.2018.12.019
342. Spatial modeling of multi-hazard threat to cultural heritage sites L. Lombardo et al. 10.1016/j.enggeo.2020.105776
343. Evaluation of the influence of source and spatial resolution of DEMs on derivative products used in landslide mapping R. Mahalingam & M. Olsen 10.1080/19475705.2015.1115431
344. Markov–Switching Spatio–Temporal Generalized Additive Model for Landslide Susceptibility A. Sridharan et al. 10.1016/j.envsoft.2023.105892
345. A review of statistically-based landslide susceptibility models P. Reichenbach et al. 10.1016/j.earscirev.2018.03.001
346. Assessment of Landslide Susceptibility Using Different Machine Learning Methods in Longnan City, China J. Gao et al. 10.3390/su142416716
347. Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis K. Hu et al. 10.1016/j.ipm.2019.02.014
348. Effects of forest cover, topography, and sampling extent on the measured density of shallow, translational landslides D. Miller & K. Burnett 10.1029/2005WR004807
349. Cost-based analysis of mitigation measures for shallow-landslide risk reduction strategies J. Galve et al. 10.1016/j.enggeo.2016.09.002
350. 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. 10.1007/s11629-013-2786-2
351. 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 10.1002/cjg2.1761
352. Application of expert rules in indirect approaches for landslide susceptibility assessment Y. Thiery et al. 10.1007/s10346-013-0390-8
353. Modeling shallow landslides and root reinforcement: A review I. Murgia et al. 10.1016/j.ecoleng.2022.106671
354. Early identification of an impending rockslide location via a spatially-aided Gaussian mixture model S. Zhou et al. 10.1214/20-AOAS1326
355. Estimating global landslide susceptibility and its uncertainty through ensemble modeling A. Felsberg et al. 10.5194/nhess-22-3063-2022
356. Setting up evaluate indicators for slope control engineering based on spatial clustering analysis W. Wang 10.1007/s11069-018-3332-x
357. Spatiotemporal distribution of labeled data can bias the validation and selection of supervised learning algorithms: A marine remote sensing example A. Stock 10.1016/j.isprsjprs.2022.02.023
358. Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach J. Deppner & M. Cajias 10.1007/s11146-022-09915-y
359. Landslide susceptibility mapping using GIS and weighted overlay method: a case study from NW Himalayas, Pakistan M. Basharat et al. 10.1007/s12517-016-2308-y
360. Focusing on the spatial non-stationarity of landslide predisposing factors in northern Iceland T. Feuillet et al. 10.1177/0309133314528944
361. An inventory-driven rock glacier status model (intact vs. relict) for South Tyrol, Eastern Italian Alps C. Kofler et al. 10.1016/j.geomorph.2019.106887
362. Landslide susceptibility mapping along national highway-1 in Jammu and Kashmir State (India) G. Hussain et al. 10.1007/s41062-019-0245-9
363. Landslide intelligent prediction using object-oriented method X. Wang & R. Niu 10.1016/j.soildyn.2010.06.017
364. Functional soil-landscape modelling to estimate slope stability in a steep Andean mountain forest region M. Ließ et al. 10.1016/j.geomorph.2011.05.015
365. Comparison of general kernel, multiple kernel, infinite ensemble and semi-supervised support vector machines for landslide susceptibility prediction Z. Fang et al. 10.1007/s00477-022-02208-z
366. Comparison of spatial and aspatial logistic regression models for landmine risk mapping C. Schultz et al. 10.1016/j.apgeog.2015.11.005
367. Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment H. Li et al. 10.3390/ijgi8080332
368. Landslides in Tijuana, Mexico: hazard assessment in an urban neighborhood A. Oliva González et al. 10.14483/22487638.17882
369. Review on remote sensing methods for landslide detection using machine and deep learning A. Mohan et al. 10.1002/ett.3998
370. Landslide Hazard and Exposure Modelling in Data‐Poor Regions: The Example of the Rohingya Refugee Camps in Bangladesh R. Emberson et al. 10.1029/2020EF001666
371. GIS-Based Landslide Susceptibility Mapping for Land Use Planning and Risk Assessment A. Roccati et al. 10.3390/land10020162
372. Potential Controlling Factors and Landslide Susceptibility Features of the 2022 Ms 6.8 Luding Earthquake S. Ma et al. 10.3390/rs16152861
373. Landslide Susceptibility Assessment Using a CNN–BiLSTM-AM Model X. Ju et al. 10.3390/su16219476
374. Replication of a terrain stability mapping using an Artificial Neural Network M. Pavel et al. 10.1016/j.geomorph.2007.08.012
375. Spatial clustering and modelling for landslide susceptibility mapping in the north of the Kathmandu Valley, Nepal B. Pokharel et al. 10.1007/s10346-020-01558-5
376. Integration of Analytical Hierarchy Process and Landslide Susceptibility Index Based Landslide Susceptibility Assessment of the Pearl River Delta Area, China H. Zhang et al. 10.1109/JSTARS.2019.2938554
377. Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping N. Micheletti et al. 10.1007/s11004-013-9511-0
378. A global slope unit-based method for the near real-time prediction of earthquake-induced landslides H. Tanyas et al. 10.1016/j.geomorph.2018.10.022
379. 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
380. Application of different earthquake-induced landslide hazard assessment models on the 2022 Ms 6.8 luding earthquake Y. Lu et al. 10.3389/feart.2024.1429421
381. Logistic regression applied to natural hazards: rare event logistic regression with replications M. Guns & V. Vanacker 10.5194/nhess-12-1937-2012
382. A Novel Relevance Vector Machine Classifier with Cuckoo Search Optimization for Spatial Prediction of Landslides N. Hoang & D. Tien Bui 10.1061/(ASCE)CP.1943-5487.0000557
383. A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran) S. Emami et al. 10.1007/s10064-020-01915-7
384. A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland M. Marmion et al. 10.1002/esp.1695
385. Assessing the quality of landslide susceptibility maps – case study Lower Austria H. Petschko et al. 10.5194/nhess-14-95-2014
386. On the dangers of model complexity without ecological justification in species distribution modeling D. Bell & D. Schlaepfer 10.1016/j.ecolmodel.2016.03.012
387. Community perceptions of landslide risk and susceptibility: a multi-country study M. Bukhari et al. 10.1007/s10346-023-02027-5
388. Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues F. Catani et al. 10.5194/nhess-13-2815-2013
389. Integration of Remotely Sensed Soil Sealing Data in Landslide Susceptibility Mapping T. Luti et al. 10.3390/rs12091486
390. Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS D. Tien Bui et al. 10.1016/j.cageo.2011.10.031
391. Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale S. Oliveira et al. 10.5194/nhess-17-1091-2017
392. GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms S. Ali et al. 10.1016/j.gsf.2020.09.004
393. A test of transferability for landslides susceptibility models under extreme climatic events: application to the Messina 2009 disaster L. Lombardo et al. 10.1007/s11069-014-1285-2
394. Reliability of groundwater vulnerability maps obtained through statistical methods A. Sorichetta et al. 10.1016/j.jenvman.2010.12.009
395. Applications of geographical information systems and remote sensing in natural disaster hazard assessment and mitigation in Taiwan H. Merrett & W. Chen 10.1080/19475705.2012.686064
396. Accounting for covariate distributions in slope-unit-based landslide susceptibility models. A case study in the alpine environment G. Amato et al. 10.1016/j.enggeo.2019.105237
397. Rock mass geomechanical properties to improve rockfall susceptibility assessment: a case study in Valchiavenna (SO) G. Bajni et al. 10.1088/1755-1315/833/1/012180
398. Modelling landslide hazards under global changes: the case of a Pyrenean valley S. Bernardie et al. 10.5194/nhess-21-147-2021
399. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model H. Hussin et al. 10.1016/j.geomorph.2015.10.030
400. Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China X. Shao et al. 10.5194/gmd-16-5113-2023
401. Determining Pleiades satellite data capability for tree diversity modeling H. Akbari & S. Kalbi 10.3832/ifor1884-009
402. A new method of pseudo absence data generation in landslide susceptibility mapping with a case study of Shenzhen C. Xiao et al. 10.1007/s11431-010-3219-x
403. Shifting from traditional landslide occurrence modeling to scenario estimation with a “glass-box” machine learning F. Caleca et al. 10.1016/j.scitotenv.2024.175277
404. A Survey of Internet of Things (IoT) for Geohazard Prevention: Applications, Technologies, and Challenges G. Mei et al. 10.1109/JIOT.2019.2952593
405. Development of a landslide susceptibility assessment for a rail network K. Martinović et al. 10.1016/j.enggeo.2016.10.011
406. Fine Identification of Landslide Acceleration Phase Using Time Logarithm Prediction Method Based on Arc Synthetic Aperture Radar Monitoring Data C. Li et al. 10.3390/app15042147
407. Machine Learning for Gully Feature Extraction Based on a Pan-Sharpened Multispectral Image: Multiclass vs. Binary Approach K. Phinzi et al. 10.3390/ijgi9040252
408. Mapping Post-Earthquake Landslide Susceptibility: A U-Net Like Approach Y. Chen et al. 10.3390/rs12172767
409. Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion Y. Garosi et al. 10.1016/j.scitotenv.2019.02.093
410. Advancing Landslide Susceptibility Mapping in the Medea Region Using a Hybrid Metaheuristic ANFIS Approach F. Debiche et al. 10.3390/land13060889
411. Landslide Detection Using Densely Connected Convolutional Networks and Environmental Conditions H. Cai et al. 10.1109/JSTARS.2021.3079196
412. Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping B. Feizizadeh et al. 10.1007/s12517-017-2918-z
413. Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping A. Zhu et al. 10.1016/j.catena.2018.07.012
414. 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. 10.1080/10106049.2021.1939440
415. GIS-based spatial prediction of landslide susceptibility using logistic regression model S. Mousavi et al. 10.1080/19475705.2010.532975
416. Prediction model for cyanide soil pollution in artisanal gold mining area by using logistic regression L. Razanamahandry et al. 10.1016/j.catena.2017.11.018
417. How does forest structure affect root reinforcement and susceptibility to shallow landslides? C. Moos et al. 10.1002/esp.3887
418. Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization X. Zhou et al. 10.1016/j.gsf.2021.101211
419. Application of two fuzzy models using knowledge-based and linear aggregation approaches to identifying flooding-prone areas in Tehran M. Rezaei et al. 10.1007/s11069-019-03816-9
420. High-resolution earthquake-induced landslide hazard assessment in Southwest China through frequency ratio analysis and LightGBM Y. Wang et al. 10.1016/j.jag.2024.103947
421. High Resolution Precipitation and Soil Moisture Data Integration for Landslide Susceptibility Mapping Y. Peiro et al. 10.3390/geosciences14120330
422. Neural network-based model for landslide susceptibility and soil longitudinal profile analyses: Two case studies F. Farrokhzad et al. 10.1016/j.jafrearsci.2011.09.004
423. Effects of sampling intensity and non-slide/slide sample ratio on the occurrence probability of coseismic landslides X. Shao et al. 10.1016/j.geomorph.2020.107222
424. 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
425. A support vector machine model of landslide susceptibility mapping based on hyperparameter optimization using the Bayesian algorithm: a case study of the highways in the southern Qinghai–Tibet Plateau K. Sun et al. 10.1007/s11069-024-06665-3
426. Mass movement susceptibility assessment of alpine infrastructure in the Salzkammergut area, Austria L. Abad et al. 10.1016/j.ijdrr.2022.103009
427. An analysis of landslide susceptibility zonation using a subjective geomorphic mapping and existing landslides M. Pavel et al. 10.1016/j.cageo.2010.10.006
428. Coseismic landslides triggered by the 2022 Luding Ms6.8 earthquake, China L. Dai et al. 10.1007/s10346-023-02061-3
429. Population Based Binary Reclassification of Indian Standard Landslide Hazard Model K. Thrideep et al. 10.1007/s12594-017-0581-3
430. Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy L. Wang et al. 10.1016/j.cageo.2013.04.006
431. Combining spatial data in landslide reactivation susceptibility mapping: A likelihood ratio-based approach in W Belgium O. Dewitte et al. 10.1016/j.geomorph.2010.06.010
432. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods H. Pourghasemi & M. Rossi 10.1007/s00704-016-1919-2
433. Multihazard Risk Model for Reliability Analysis of Expansive Soil Landslide Based on T–S Fuzzy Logic J. Lu et al. 10.1061/AJRUA6.0001225
434. Investigations of a Weathered and Closely Jointed Rock Slope Failure Using Back Analyses K. Shao et al. 10.3390/su132313452
435. 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 10.1007/s41748-019-00114-z
436. Damage Assessment of Earthen Sites of the Ming Great Wall in Qinghai Province Y. Du et al. 10.1145/3376120
437. Characteristics of landslides and assessment of deep-seated landslide susceptibility in Northern Turkey T. Duman & T. Çan 10.1007/s42990-023-00105-3
438. Forest harvesting is associated with increased landslide activity during an extreme rainstorm on Vancouver Island, Canada J. Goetz et al. 10.5194/nhess-15-1311-2015
439. Multivariate statistical algorithms for landslide susceptibility assessment in Kailash Sacred landscape, Western Himalaya A. Pandey et al. 10.1080/19475705.2023.2227324
440. GIS-based earthquake-triggered-landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China Y. Yi et al. 10.5194/nhess-19-1973-2019
441. Prediction of Landslide Displacement by the Novel Coupling Method of HP Filtering Method and Extreme Gradient Boosting L. Zhou et al. 10.1007/s11223-022-00470-8
442. 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. 10.3390/app14177500
443. Random forest and spatial cross-validation performance in predicting species abundance distributions C. Mushagalusa et al. 10.1186/s40068-024-00352-9
444. Landslide Susceptibility Assessment Using Conditional Analysis and Rare Events Logistics Regression: A Case-Study in the Antrodoco Area (Rieti, Italy) V. Chiessi et al. 10.4236/gep.2016.412001