570 citations as recorded by crossref.

1. The impact of DEM resolution on landslide susceptibility modeling A. Wubalem https://doi.org/10.1007/s12517-022-10241-z
2. Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential Y. Chen et al. https://doi.org/10.1080/10106049.2021.1920635
3. Landslides hazard, vulnerability and risk mapping in the data-poor region of northern Pakistan Y. Ullah et al. https://doi.org/10.1007/s12665-024-11858-x
4. LNRF-Based Landslide Zonation: Enhancing Risk Assessment in Badakhshan, Afghanistan M. Amini et al. https://doi.org/10.63333/eem.v1n14
5. A combined multi-statistical strategy for mapping groundwater spring potential in northeastern Iraq R. Qaradaghy et al. https://doi.org/10.1007/s11600-025-01715-1
6. Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks S. Ji et al. https://doi.org/10.1007/s10346-020-01353-2
7. The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China K. Zhang et al. https://doi.org/10.1007/s12665-017-6731-5
8. Selecting optimal conditioning parameters for landslide susceptibility: an experimental research on Aqabat Al-Sulbat, Saudi Arabia S. Alqadhi et al. https://doi.org/10.1007/s11356-021-15886-z
9. CBS Tabanlı Melez Makine Öğrenmesi Uygulamalarının Ani Sel Duyarlılık Haritalamasında Kullanımı Ö. EKMEKCİOĞLU https://doi.org/10.21597/jist.1225104
10. Modelos geoestaddsticos para la predicciin de fallos de una zona de la red de abastecimiento de agua de Bogott, integrando algoritmos de Machine Learning (Geostatistical Models for the Prediction of Water Supply Network Failures in Bogott, Integrating Machine Learning Algorithms) C. Navarrete-LLpez et al. https://doi.org/10.2139/ssrn.3113048
11. Significance of variable selection and scaling issues for probabilistic modeling of rainfall-induced landslide susceptibility T. Oommen et al. https://doi.org/10.1007/s41324-017-0154-y
12. A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping Q. Pham et al. https://doi.org/10.1080/19475705.2021.1944330
13. Mapping groundwater potential zones in Kanchanaburi Province, Thailand by integrating of analytic hierarchy process, frequency ratio, and random forest N. Thanh et al. https://doi.org/10.1016/j.ecolind.2022.109591
14. The Effect of Temporal Characteristics on Developing a Practical Rainfall-Induced Landslide Potential Evaluation Model Using Random Forest Method Y. Huang & S. Lu https://doi.org/10.3390/w13233348
15. BisDeNet: A New Lightweight Deep Learning-Based Framework for Efficient Landslide Detection T. Chen et al. https://doi.org/10.1109/JSTARS.2024.3351873
16. The Impact of Bamboo on Rainfall-Triggered Landslide Distribution at the Regional Scale: A Case Study from SE China Z. Guo et al. https://doi.org/10.3390/f15122223
17. Predicting Landslide Deposit Zones: Insights from Advanced Sampling Strategies in the Ilopango Caldera, El Salvador L. Calderon-Cucunuba et al. https://doi.org/10.3390/land14020269
18. Factors influencing the formation of shallow landslides in the Boston Mountains of northwest Arkansas, USA M. Maguigan et al. https://doi.org/10.1080/02723646.2015.1058731
19. Reconstructing the Historical Terrestrial Water Storage Variations in the Huang–Huai–Hai River Basin With Consideration of Water Withdrawals C. Yang et al. https://doi.org/10.3389/fenvs.2022.840540
20. Data-driven mapping of the potential mountain permafrost distribution N. Deluigi et al. https://doi.org/10.1016/j.scitotenv.2017.02.041
21. Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt A. Maxwell et al. https://doi.org/10.3390/rs12030486
22. Spatially distributed antecedent rainfall thresholds for landslide occurrence: a multitask machine learning modelling approach L. Lucchese et al. https://doi.org/10.1080/02626667.2025.2561163
23. Comparative landslide susceptibility assessment using statistical information value and index of entropy model in Bhanupali-Beri region, Himachal Pradesh, India P. Singh et al. https://doi.org/10.1007/s10668-020-00811-0
24. Comparing performance of random forest and adaptive neuro-fuzzy inference system data mining models for flood susceptibility mapping M. Vafakhah et al. https://doi.org/10.1007/s12517-020-05363-1
25. Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction S. Meshram et al. https://doi.org/10.1007/s11356-020-11335-5
26. Combining spatial modelling and regionalization of rainfall thresholds for debris flows hazard mapping in the Emilia-Romagna Apennines (Italy) C. G. et al. https://doi.org/10.1007/s10346-021-01739-w
27. Groundwater vulnerability assessment of elevated arsenic in Gangetic plain of West Bengal, India; Using primary information, lithological transport, state-of-the-art approaches D. Mishra et al. https://doi.org/10.1016/j.jconhyd.2023.104195
28. Prediction of Groundwater Level Variations in a Changing Climate: A Danish Case Study R. Gonzalez & J. Arsanjani https://doi.org/10.3390/ijgi10110792
29. Temporal validity of landslide inventories in hazard mapping: insights from Hubei Province, China Y. Zhu et al. https://doi.org/10.1007/s10346-025-02651-3
30. Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management Z. Guo et al. https://doi.org/10.1016/j.gsf.2021.101249
31. Hierarchical machine learning for the automatic classification of surface deformation from SAR observations J. Rivera-Rivera et al. https://doi.org/10.1016/j.aiig.2025.100171
32. Improving the model robustness of flood hazard mapping based on hyperparameter optimization of random forest M. Liao et al. https://doi.org/10.1016/j.eswa.2023.122682
33. Mapping specific groundwater vulnerability to nitrate using random forest: case of Sais basin, Morocco A. Lahjouj et al. https://doi.org/10.1007/s40808-020-00761-6
34. An evolutionary approach for spatial prediction of landslide susceptibility using LiDAR and symbolic classification with genetic programming P. Gorsevski https://doi.org/10.1007/s11069-021-04780-z
35. Landslide susceptibility: a statistically-based assessment on a depositional pyroclastic ramp F. Murillo-García et al. https://doi.org/10.1007/s11629-018-5225-6
36. Using machine learning algorithms to map the groundwater recharge potential zones H. Pourghasemi et al. https://doi.org/10.1016/j.jenvman.2020.110525
37. Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping M. Boroughani et al. https://doi.org/10.1016/j.ecoinf.2020.101059
38. Susceptibility assessment of environmental geological disasters in Liulin County based on RF: from the perspective of positive and negative sample proportion Z. Wang et al. https://doi.org/10.1007/s11356-023-30778-0
39. Development of rainfall-induced clustered landslide susceptibility assessment model for Zixing, Hunan based on XGBoost and SHAP K. Liu et al. https://doi.org/10.1016/j.pce.2026.104842
40. Machine learning application for prediction of sonic wave transit time - A case of Niger Delta basin O. Akinyemi et al. https://doi.org/10.1016/j.rineng.2023.101528
41. The added value of a regional landslide susceptibility assessment: The western branch of the East African Rift A. Depicker et al. https://doi.org/10.1016/j.geomorph.2019.106886
42. Impact of Spatial Resolution of Digital Elevation Model on Landslide Susceptibility Mapping: A Case Study in Kullu Valley, Himalayas S. Meena & T. Gudiyangada Nachappa https://doi.org/10.3390/geosciences9080360
43. Slope Unit Maker (SUMak): an efficient and parameter-free algorithm for delineating slope units to improve landslide modeling J. Woodard et al. https://doi.org/10.5194/nhess-24-1-2024
44. Assessing Consistency and Inconsistency in Landslide Susceptibility Mapping: Quality Criteria and Pixel-Level Analysis in a Case Study from the Alborz Mountains, North of Tehran, Iran T. Kreuzer et al. https://doi.org/10.1007/s11069-025-07487-7
45. The role of conditioning factors in machine learning-based landslide spatial probability B. Nguyen & V. Doan https://doi.org/10.1680/jenge.25.00077
46. Spatial patterns of landslide dimension: A tool for magnitude mapping F. Catani et al. https://doi.org/10.1016/j.geomorph.2016.08.032
47. Spatial prediction of landslide susceptibility in Taleghan basin, Iran M. Mokhtari & S. Abedian https://doi.org/10.1007/s00477-019-01696-w
48. Integration of Remotely Sensed Soil Sealing Data in Landslide Susceptibility Mapping T. Luti et al. https://doi.org/10.3390/rs12091486
49. Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion O. Rahmati et al. https://doi.org/10.1016/j.geomorph.2017.09.006
50. Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest E. Sahin https://doi.org/10.1007/s42452-020-3060-1
51. Mapping landslide phenomena in landlocked developing countries by means of satellite remote sensing data: the case of Dilijan (Armenia) area S. Bianchini et al. https://doi.org/10.1080/19475705.2016.1189459
52. A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data M. Tehrany et al. https://doi.org/10.1007/s00704-018-2628-9
53. Assessment and prediction of hexavalent chromium vulnerability in groundwater by Geochemical modelling, NOBLES Index and Random Forest Model A. Raj et al. https://doi.org/10.1016/j.scitotenv.2023.167570
54. Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India B. Pham et al. https://doi.org/10.1007/s40710-017-0248-5
55. Enhancing geohazard susceptibility mapping with a sample reliability algorithm: a case study in Guangzhou, China Y. Jiang et al. https://doi.org/10.1007/s10064-026-04786-6
56. Integrated approach for landslide hazard assessment in the High City of Antananarivo, Madagascar (UNESCO tentative site) W. Frodella et al. https://doi.org/10.1007/s10346-022-01933-4
57. Prediction of annual groundwater depletion: An investigation of natural and anthropogenic influences V. Gholami et al. https://doi.org/10.1007/s12040-023-02184-0
58. Effects of raster resolution on real probability of landslides X. Shao et al. https://doi.org/10.1016/j.rsase.2020.100364
59. Deep neural network framework for predicting debris flow entrainment growth rate in diverse terrain conditions C. Song et al. https://doi.org/10.1007/s10346-026-02777-y
60. Combination of Rainfall Thresholds and Susceptibility Maps for Dynamic Landslide Hazard Assessment at Regional Scale S. Segoni et al. https://doi.org/10.3389/feart.2018.00085
61. Quantitative Assessment of Landslide Susceptibility Comparing Statistical Index, Index of Entropy, and Weights of Evidence in the Shangnan Area, China J. Liu & Z. Duan https://doi.org/10.3390/e20110868
62. A comparative analysis of weight-based machine learning methods for landslide susceptibility mapping in Ha Giang area T. Trinh et al. https://doi.org/10.1080/20964471.2022.2043520
63. Landslide hazard prediction under an extreme rainfall scenario by considering multiple timescale rainfalls and effective recharge Z. Guo et al. https://doi.org/10.1080/17499518.2025.2570863
64. Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm D. Tien Bui et al. https://doi.org/10.3390/rs11080931
65. Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region Y. Yi et al. https://doi.org/10.1016/j.catena.2020.104851
66. Unveiling the drivers of rainfall-triggered landslide spatial distribution: Insights from event-based and historical landslide inventories in Lisbon region, Portugal C. Villaça et al. https://doi.org/10.1016/j.geomorph.2026.110222
67. A comparative assessment of fuzzy logic and evidential belief function models for mapping artesian zone boundary in an arid region, Iraq A. Al-Abadi et al. https://doi.org/10.2166/hydro.2017.022
68. Comparison of multiple conventional and unconventional machine learning models for landslide susceptibility mapping of Northern part of Pakistan B. Aslam et al. https://doi.org/10.1007/s10668-022-02314-6
69. Advanced machine learning algorithms for flood susceptibility modeling — performance comparison: Red Sea, Egypt A. Youssef et al. https://doi.org/10.1007/s11356-022-20213-1
70. Application of geophysical and multispectral imagery data for predictive mapping of a complex geo-tectonic unit: a case study of the East Vardar Ophiolite Zone, North-Macedonia F. Arnaut et al. https://doi.org/10.1007/s12145-024-01243-4
71. Assessing landslide susceptibility, analyzing and ranking causes. Case study of the northeastern region of Bouira-Djebahia, Algeria N. Dilmi & H. Boutabba https://doi.org/10.2298/GSGD2301157D
72. Geological Hazards Susceptibility Evaluation Based on GA‐BPNN: A Case Study of Xingye County K. Xu et al. https://doi.org/10.1029/2019EA000929
73. A combined analysis of frequency ratio and analytical hierarchy process for landslide susceptibility assessment in Tenta, South Wollo, Ethiopia A. Ali et al. https://doi.org/10.1038/s41598-025-94611-z
74. Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China P. Tsangaratos et al. https://doi.org/10.1007/s10346-016-0769-4
75. Unraveling the drivers of intensified landslide regimes in Western Ghats, India A. Yunus et al. https://doi.org/10.1016/j.scitotenv.2021.145357
76. Integration of active tectonic index and geomorphic parameters for landslides susceptibility mapping in the Barak River basin A. Ashuli et al. https://doi.org/10.1007/s11069-025-07559-8
77. 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. https://doi.org/10.1016/j.scitotenv.2020.140549
78. Modeling Wetland Habitat Quality in the Rarh Tract of Eastern India R. Khatun & S. Das https://doi.org/10.1007/s13157-024-01849-w
79. Spatio-temporal variability of landslides as indicator of climate change impact: Towards an Italian national scale integrated procedure for the rainfall role analysis in landslide dynamics M. Delchiaro et al. https://doi.org/10.1016/j.ijdrr.2026.106252
80. Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps S. Steger et al. https://doi.org/10.1016/j.geomorph.2016.03.015
81. Integrated Geospatial Assessment of a Human-Induced Winter Landslide in Almaty: The February 2024 Tau-Samal Event E. Orynbassarova et al. https://doi.org/10.3390/su18115691
82. Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) B. Kalantar et al. https://doi.org/10.1080/19475705.2017.1407368
83. Evaluation of Erosion Susceptibility of Godrahav Basin (Artvin) Using Geo-environmental Factors E. ÇORUHLU & H. AKINCI https://doi.org/10.48123/rsgis.983373
84. Landslide susceptibility mapping using modified frequancy ratio method in Correb area, South Wollo, North-Western Ethiopia A. Ali https://doi.org/10.1007/s44288-024-00053-x
85. Land-subsidence susceptibility zonation using remote sensing, GIS, and probability models in a Google Earth Engine platform Z. Najafi et al. https://doi.org/10.1007/s12665-020-09238-2
86. A Downscaling–Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM B. Tian et al. https://doi.org/10.3390/rs15184601
87. Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process H. Pourghasemi et al. https://doi.org/10.1016/j.gsf.2020.03.005
88. Landslide susceptibility in the Belt and Road Countries: continental step of a multi-scale approach G. Titti et al. https://doi.org/10.1007/s12665-021-09910-1
89. AI-Powered Digital Twin Technology for Highway System Slope Stability Risk Monitoring J. Xu & Y. Zhang https://doi.org/10.3390/geotechnics5010019
90. How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region T. Saha et al. https://doi.org/10.1016/j.jenvman.2021.113344
91. Correlation Between Geotechnical Indexes and Landslide Occurrence in Southwestern Cyprus Using GIS and Machine Learning P. Tzampoglou et al. https://doi.org/10.1007/s10706-024-03031-9
92. Characteristics and susceptibility assessment of landslides triggered by historical mega earthquakes in the northeastern margin of the qinghai-tibet plateau: a case study of the 1927 gulang M8 earthquake S. Liu et al. https://doi.org/10.1080/19475705.2026.2700846
93. Sampling strategies for machine learning-based linear erosion studies: a review approaching contributing area T. Olivatto & J. Di Lollo https://doi.org/10.1016/j.nhres.2025.08.002
94. Definition of Environmental Indicators for a Fast Estimation of Landslide Risk at National Scale S. Segoni & F. Caleca https://doi.org/10.3390/land10060621
95. A data-driven approach to generate past GRACE-like terrestrial water storage solution by calibrating the land surface model simulations W. Jing et al. https://doi.org/10.1016/j.advwatres.2020.103683
96. Geospatial Analysis of Mass-Wasting Susceptibility of Four Small Catchments in Mountainous Area of Miyun County, Beijing C. Cao et al. https://doi.org/10.3390/ijerph16152801
97. Extending GRACE terrestrial water storage anomalies by combining the random forest regression and a spatially moving window structure W. Jing et al. https://doi.org/10.1016/j.jhydrol.2020.125239
98. Assessment of temporal probability for rainfall-induced landslides based on nonstationary extreme value analysis H. Kim et al. https://doi.org/10.1016/j.enggeo.2021.106372
99. Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran S. Farhadi et al. https://doi.org/10.3390/min12060689
100. GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models W. Chen et al. https://doi.org/10.1080/19475705.2017.1289250
101. Assessing scale‐dependent effects on Forest biomass productivity based on machine learning J. He et al. https://doi.org/10.1002/ece3.9110
102. Landslide susceptibility prediction using frequency ratio model: a case study of Uttarakhand, Himalaya (India) P. Singh et al. https://doi.org/10.1007/s43538-023-00171-z
103. Landslide susceptibility modelling using the quantitative random forest method along the northern portion of the Yukon Alaska Highway Corridor, Canada P. Behnia & A. Blais-Stevens https://doi.org/10.1007/s11069-017-3104-z
104. Seven-Day Landslide Forecasting from PCA-Derived InSAR Data with a Random Forest Classifier A. Garg https://doi.org/10.1007/s41748-026-01227-y
105. Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria P. Lima et al. https://doi.org/10.1007/s10346-021-01693-7
106. Advanced hyperparameter optimization for improved spatial prediction of shallow landslides using extreme gradient boosting (XGBoost) T. Kavzoglu & A. Teke https://doi.org/10.1007/s10064-022-02708-w
107. Prediction of total organic carbon at Rumaila oil field, Southern Iraq using conventional well logs and machine learning algorithms A. Handhal et al. https://doi.org/10.1016/j.marpetgeo.2020.104347
108. Utilizing artificial intelligence techniques for soil depth prediction and its influences in landslide hazard modeling A. Pradhan et al. https://doi.org/10.1007/s00477-024-02765-5
109. Boot-Shaped Terrain Screening and Deep Learning Semantic Segmentation for Landslide-Hazard Candidate Extraction from Airborne LiDAR DEM: A Case Study in Zhenxiong County, China B. Du et al. https://doi.org/10.3390/rs18172983
110. Towards automatic delineation of landslide source and runout K. Bhuyan et al. https://doi.org/10.1016/j.enggeo.2024.107866
111. 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. https://doi.org/10.1007/s10346-015-0614-1
112. Spatial Modelling of Groundwater Potential Zones Using GIS-Based Machine Learning Techniques: A Case Study of Abuja, Nigeria D. Ibrahim et al. https://doi.org/10.3390/geosciences16050195
113. A spatial model for sporadic tree species distribution in support of tree oriented silviculture D. Melini https://doi.org/10.12899/asr-751
114. Projected Rainfall Triggered Landslide Susceptibility Changes in the Hengduan Mountain Region, Southwest China under 1.5–4.0 °C Warming Scenarios Based on CMIP6 Models H. Yin et al. https://doi.org/10.3390/atmos14020214
115. Automated classification of A-DInSAR-based ground deformation by using random forest D. Festa et al. https://doi.org/10.1080/15481603.2022.2134561
116. Machine learning prediction of post-earthquake debris flows incorporating hydrological variables X. Wang et al. https://doi.org/10.1007/s11069-026-07970-9
117. Susceptibility of intrusion-related landslides at volcanic islands: the Stromboli case study F. Di Traglia et al. https://doi.org/10.1007/s10346-017-0866-z
118. A physics-guided neural network for reliable slope stability assessment under data scarcity D. Xu & Z. Wang https://doi.org/10.1016/j.compgeo.2026.108576
119. A comparative study of empirical and machine learning approaches for soil thickness mapping in the Joshimath region (India) K. Gupta et al. https://doi.org/10.1016/j.catena.2024.108024
120. Multi-scale analysis of the susceptibility of different landslide types and identification of the main controlling factors Y. Yang et al. https://doi.org/10.1016/j.ecolind.2024.112797
121. Estimation of Coastal Currents Using a Soft Computing Method: A Case Study in Galway Bay, Ireland L. Ren et al. https://doi.org/10.3390/jmse7050157
122. Wildfire assessment using machine learning algorithms in different regions S. Moghim & M. Mehrabi https://doi.org/10.1186/s42408-024-00335-2
123. Landslides in Tijuana, Mexico: hazard assessment in an urban neighborhood A. Oliva González et al. https://doi.org/10.14483/22487638.17882
124. Bayes Olasılık Modeli ve Frekans Oranı (FO) Yöntemi ile Esmahanım Deresi Havzası’nın (Düzce) Heyelan Duyarlık Analizi V. Avci https://doi.org/10.48123/rsgis.1708584
125. Analysis of gully erosion susceptibility and spatial modelling using a GIS-based approach Y. Wei et al. https://doi.org/10.1016/j.geoderma.2022.115869
126. Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China X. Hu et al. https://doi.org/10.1007/s11069-020-04371-4
127. Investigating the dynamic nature of landslide susceptibility in the Indian Himalayan region A. Sharma & H. Sandhu https://doi.org/10.1007/s10661-024-12440-5
128. The (f)utility to account for pre-failure topography in data-driven landslide susceptibility modelling S. Steger et al. https://doi.org/10.1016/j.geomorph.2020.107041
129. A Data-Driven Hybrid Approach Integrating Frequency Ratio, Analytic Hierarchy Process, and Random Forest with Entropy-Based Information Gain-Derived Weights for Landslide Susceptibility Assessment M. Dwivedi et al. https://doi.org/10.1007/s12524-026-02544-5
130. Regional-scale landslide risk assessment in Central Asia F. Caleca et al. https://doi.org/10.5194/nhess-24-13-2024
131. Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features S. Naghibi & M. Moradi Dashtpagerdi https://doi.org/10.1007/s10040-016-1466-z
132. Land subsidence susceptibility assessment using random forest machine learning algorithm M. Mohammady et al. https://doi.org/10.1007/s12665-019-8518-3
133. Machine Learning for Defining the Probability of Sentinel-1 Based Deformation Trend Changes Occurrence P. Confuorto et al. https://doi.org/10.3390/rs14071748
134. Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models H. Hong et al. https://doi.org/10.1016/j.geomorph.2016.02.012
135. Landslide susceptibility map refinement using PSInSAR data A. Ciampalini et al. https://doi.org/10.1016/j.rse.2016.07.018
136. Towards landslide space-time forecasting through machine learning: the influence of rainfall parameters and model setting N. Nocentini et al. https://doi.org/10.3389/feart.2023.1152130
137. Evaluation of predictive models for post-fire debris flow occurrence in the western United States E. Nikolopoulos et al. https://doi.org/10.5194/nhess-18-2331-2018
138. Decadal Changes in Greenland Ice Sheet Firn Aquifers from Radar Scatterometer X. Shang et al. https://doi.org/10.3390/rs14092134
139. Explainable Boosting Machines for Slope Failure Spatial Predictive Modeling A. Maxwell et al. https://doi.org/10.3390/rs13244991
140. Automated Machine Learning-Based Landslide Susceptibility Mapping for the Three Gorges Reservoir Area, China J. Ma et al. https://doi.org/10.1007/s11004-023-10116-3
141. Machine learning based landslide assessment of the Belgrade metropolitan area: Pixel resolution effects and a cross-scaling concept U. Đurić et al. https://doi.org/10.1016/j.enggeo.2019.05.007
142. Water Resources Management Through Flood Spreading Project Suitability Mapping Using Frequency Ratio, k-nearest Neighbours, and Random Forest Algorithms S. Naghibi et al. https://doi.org/10.1007/s11053-019-09530-4
143. Analysing the relationship between rainfalls and landslides to define a mosaic of triggering thresholds for regional-scale warning systems S. Segoni et al. https://doi.org/10.5194/nhess-14-2637-2014
144. Evaluating the Effects of Digital Elevation Models in Landslide Susceptibility Mapping in Rangamati District, Bangladesh Y. Rabby et al. https://doi.org/10.3390/rs12172718
145. Analysis of the Role of Precipitation and Land Use on the Size of the Source Area of Shallow Landslides A. Giarola et al. https://doi.org/10.3390/w15193340
146. Feature-based model for landslide susceptibility mapping using a multi-parametric decision-making technique and the analytic hierarchy process L. Bopche & P. Rege https://doi.org/10.1007/s12046-021-01648-7
147. 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. https://doi.org/10.1007/s10346-024-02287-9
148. The influence of the selection of non-geological disasters sample spatial range on the evaluation of environmental geological disasters susceptibility: a case study of Liulin County J. Chen et al. https://doi.org/10.1007/s11356-023-25454-2
149. Geospatial modeling of landslide susceptibility in Debek, South Wollo, Ethiopia: comparative analysis of frequency ratio and analytical hierarchy process models for geohazards management A. Ali et al. https://doi.org/10.3389/feart.2025.1557860
150. Construction of landslide warning by combining rainfall threshold and landslide susceptibility in the gully region of the Loess Plateau: A case of Lanzhou City, China H. Shu et al. https://doi.org/10.1016/j.jhydrol.2024.132148
151. Graph-Represented Broad Learning System for Landslide Susceptibility Mapping in Alpine-Canyon Region L. Xu et al. https://doi.org/10.3390/rs14122773
152. Identification of groundwater potential zones of Idukki district using remote sensing and GIS-based machine-learning approach Z. Khan & B. Jhamnani https://doi.org/10.2166/ws.2023.134
153. Important considerations in machine learning-based landslide susceptibility assessment under future climate conditions Y. Han & S. Semnani https://doi.org/10.1007/s11440-024-02363-3
154. GIS-based statistical analysis for landslide susceptibility evaluation and zonation mapping: A case from Blue Nile Gorge, Gohatsion-Dejen road corridor, Central Ethiopia Y. Ali et al. https://doi.org/10.1016/j.envc.2024.100968
155. Landslide Hazard Zonation Driven by Multi-Rainfall Scenarios Based on the Optimal XGBoost Model—A Case Study of Yongren County, Yunnan Province, China Z. Zeng et al. https://doi.org/10.3390/su172411307
156. Landslide susceptibility mapping along Rishikesh–Badrinath national highway (Uttarakhand) by applying multi-criteria decision-making (MCDM) approach M. Ramiz et al. https://doi.org/10.1007/s12665-023-11268-5
157. Impact of antecedent rainfall and soil saturation on widespread debris flows in the northern Western Ghats during the 2021 extreme rainfall S. Islam et al. https://doi.org/10.1007/s10064-025-04383-z
158. Decoding vegetation's role in landslide susceptibility mapping: An integrated review of techniques and future directions Y. Li & W. Duan https://doi.org/10.1016/j.bgtech.2023.100056
159. Basin-wide flood depth and exposure mapping from SAR images and machine learning models C. Hao et al. https://doi.org/10.1016/j.jenvman.2021.113367
160. Decision-Making Techniques for Water Shutoff Using Random Forests and Its Application in High Water Cut Reservoirs K. Seddiqi et al. https://doi.org/10.1021/acsomega.1c03973
161. Chemical process fault diagnosis based on enchanted machine‐learning approach X. Yang et al. https://doi.org/10.1002/cjce.23642
162. Enhancing flood susceptibility mapping through integration of nature-inspired metaheuristic algorithms and machine learning M. Hassanvand et al. https://doi.org/10.1016/j.envc.2026.101479
163. 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 https://doi.org/10.1007/s00704-016-1919-2
164. An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method S. Shrestha et al. https://doi.org/10.3390/ijgi6110365
165. GIS-based ensemble soft computing models for landslide susceptibility mapping B. Pham et al. https://doi.org/10.1016/j.asr.2020.05.016
166. Machine Learning‐based landslide susceptibility analysis in volcanic islands: A comparison with traditional approaches S. Alfaya et al. https://doi.org/10.1016/j.gespch.2026.100018
167. Climate change-induced shifts in landslide susceptibility in São Sebastião (southeastern Brazil) E. Alcântara et al. https://doi.org/10.1016/j.nhres.2024.11.005
168. Stand and tree characteristics influence damage severity after a catastrophic hurricane disturbance C. Fortuin et al. https://doi.org/10.1016/j.foreco.2023.120844
169. 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. https://doi.org/10.1016/j.trgeo.2025.101731
170. Double-threshold validation tool (DTVT): From landslide hazard maps to operational early warning systems N. Nocentini et al. https://doi.org/10.1016/j.ijdrr.2025.105786
171. A Stacking Ensemble Approach for Postdisaster Building Damage Assessment S. Lin et al. https://doi.org/10.1061/JCCEE5.CPENG-7005
172. A novel hybrid model integrating high resolution remote sensing and stacking ensemble techniques for landslide susceptibility mapping: Application to event-based landslide inventory Z. Guo et al. https://doi.org/10.1016/j.geomorph.2025.109886
173. Relevant geological-geotechnical parameters to evaluate the terrain susceptibility for shallow landslides: Nova Friburgo, Rio de Janeiro, Brazil R. da Silva et al. https://doi.org/10.1007/s10064-021-02557-z
174. Mapping the spatial transmission risk and public spatial awareness in the use of personal protective equipment: COVID-19 pandemic in East Java, Indonesia P. Purwanto et al. https://doi.org/10.1016/j.ijdrr.2023.104018
175. Rainfall-Induced Shallow Landslide Susceptibility Mapping at Two Adjacent Catchments Using Advanced Machine Learning Algorithms A. Pradhan & Y. Kim https://doi.org/10.3390/ijgi9100569
176. Is higher resolution always better? A comparison of open-access DEMs for optimized slope unit delineation and regional landslide prediction M. Ahmed et al. https://doi.org/10.5194/nhess-25-2519-2025
177. Integrating empirical models and satellite radar can improve landslide detection for emergency response K. Burrows et al. https://doi.org/10.5194/nhess-21-2993-2021
178. Impact of extreme rainfall on triggering conditions and susceptibility for shallow landslides: a case study in the Alpes-Maritimes region (France) L. Armand et al. https://doi.org/10.5194/nhess-26-3987-2026
179. Evaluating the applicability of simulated soil moisture index in forecasting post-earthquake debris flows Z. Wei et al. https://doi.org/10.1016/j.enggeo.2026.108557
180. Evaluating the effectiveness and robustness of machine learning models with varied geo-environmental factors for determining vulnerability to water flow-induced gully erosion F. Aboutaib et al. https://doi.org/10.3389/fenvs.2023.1207027
181. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model H. Hussin et al. https://doi.org/10.1016/j.geomorph.2015.10.030
182. Streamflow forecasting using machine learning and remote sensing data in the Himalayan region N. Suwal et al. https://doi.org/10.2166/wpt.2025.153
183. Impact of negative sampling strategies on landslide susceptibility assessment Y. Zhu et al. https://doi.org/10.1016/j.asr.2025.04.070
184. Updating EWS rainfall thresholds for the triggering of landslides A. Rosi et al. https://doi.org/10.1007/s11069-015-1717-7
185. The effectiveness of high-resolution LiDAR data combined with PSInSAR data in landslide study A. Ciampalini et al. https://doi.org/10.1007/s10346-015-0663-5
186. Estimation of the susceptibility of a road network to shallow landslides with the integration of the sediment connectivity M. Bordoni et al. https://doi.org/10.5194/nhess-18-1735-2018
187. Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China W. Chen et al. https://doi.org/10.1016/j.scitotenv.2018.01.124
188. Modelling the rainfall threshold for shallow landslides considering the landslide predisposing factors in Portugal C. Villaça et al. https://doi.org/10.1007/s10346-024-02284-y
189. Application of coupling physics–based model TRIGRS with random forest in rainfall-induced landslide-susceptibility assessment L. Yang et al. https://doi.org/10.1007/s10346-024-02276-y
190. Potentialities and development of groundwater resources applying machine learning models in the extended section of Manbhum-Singhbhum Plateau, India A. Ghosh & B. Bera https://doi.org/10.1016/j.hydres.2023.11.002
191. Comparative multi-criteria decision-making approaches for landslide susceptibility mapping in Khagrachhari district of southeastern Bangladesh M. Rahman et al. https://doi.org/10.1007/s44288-025-00346-9
192. Landslide Susceptibility Mapping Using Different Modeling Approaches in Forested Areas (Sample of Çankırı-Yapraklı) E. BUĞDAY https://doi.org/10.33904/ejfe.582276
193. Detection of areas prone to flood-induced landslides risk using certainty factor and its hybridization with FAHP, XGBoost and deep learning neural network R. Costache et al. https://doi.org/10.1080/10106049.2021.1973115
194. Coupled model for susceptibility mapping of hydro-fluctuation belt rock degradation in the Jinsha River reservoirs J. Zhou et al. https://doi.org/10.1080/19475705.2025.2562281
195. Deriving rainfall thresholds with XAI and GNSS measurements for a large landslide R. Franceschini et al. https://doi.org/10.1007/s10346-025-02657-x
196. Dynamic Landslide Susceptibility Assessment Integrating SBAS-InSAR and Interpretable Machine Learning: A Case Study of the Baihetan Reservoir Area, Southwest China H. Wang et al. https://doi.org/10.3390/rs18040578
197. Improvement of the predictive performance of landslide mapping models in mountainous terrains using cluster sampling M. Riaz et al. https://doi.org/10.1080/10106049.2022.2066202
198. Window-Based Morphometric Indices as Predictive Variables for Landslide Susceptibility Models N. Barbosa et al. https://doi.org/10.3390/rs13030451
199. Geotechnical and hydrological characterization of hillslope deposits for regional landslide prediction modeling G. Bicocchi et al. https://doi.org/10.1007/s10064-018-01449-z
200. Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models S. Siahkamari et al. https://doi.org/10.1080/10106049.2017.1316780
201. A framework employing the AHP and FR methods to assess the landslide susceptibility of the Western Ghats region in Kollam district B. Babitha et al. https://doi.org/10.1007/s42797-022-00061-5
202. Machine learning for predicting landslide risk of Rohingya refugee camp infrastructure N. Ahmed et al. https://doi.org/10.1080/24751839.2019.1704114
203. Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability M. Di Napoli et al. https://doi.org/10.1007/s10346-020-01392-9
204. Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques Z. Ferreira et al. https://doi.org/10.1080/19475705.2025.2471019
205. Landslide Susceptibility Mapping of High‐Risk Peripheral Slopes Around Bageshwar City Using Frequency Ratio and Random Forest Techniques A. Ahamad et al. https://doi.org/10.1002/gj.70281
206. Landslide susceptibility mapping in Al-Khawabi river basin, Tartous, Syria: An integrated approach of bivariate-statistical modelling and geospatial technology H. Abdo https://doi.org/10.1016/j.envc.2025.101327
207. Risk assessment of landslide and rockfall hazards in hilly region of southwestern China: a case study of Qijiang, Wuxi and Chishui P. Ye et al. https://doi.org/10.1007/s12665-024-11698-9
208. Spatial variability and future susceptibility of thaw hazards on hillslopes across the Qinghai-Tibet Plateau J. Yang et al. https://doi.org/10.1016/j.gloplacha.2026.105647
209. Landslide susceptibility mapping along Kolli hills Ghat road section (India) using frequency ratio, relative effect and fuzzy logic models V. Ramesh & S. Anbazhagan https://doi.org/10.1007/s12665-014-3954-6
210. Susceptibility assessment of geological hazards in Shenzhen Town, Ninghai county based on the APH-CF model S. Han et al. https://doi.org/10.3389/feart.2024.1494898
211. GIS-based landslide susceptibility mapping of the Meghalaya-Shillong Plateau region using machine learning algorithms N. Agrawal & J. Dixit https://doi.org/10.1007/s10064-023-03188-2
212. Improving ML-based landslide susceptibility using ensemble method for sample selection: a case study of Kangra district in Himachal Pradesh, India A. Singh et al. https://doi.org/10.1007/s11356-024-34726-4
213. Hybrid modelling of landslide susceptibility using frequency ratio and maximum entropy models in the MPRRU region of Jammu and Kashmir, India A. Taloor et al. https://doi.org/10.1016/j.pce.2026.104460
214. ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool D. Di Santo et al. https://doi.org/10.5194/gmd-18-433-2025
215. Influences of outliers on performance of geographically weighted random forest for modelling cadmium concentrations in topsoil of the northern part of Ireland Y. Li et al. https://doi.org/10.1016/j.jhazmat.2025.140872
216. Optimized Apriori algorithm for deformation response analysis of landslide hazards L. Linwei et al. https://doi.org/10.1016/j.cageo.2022.105261
217. Modelling flood susceptibility in northern Iran: Application of five well‐known machine‐learning models A. Kohansarbaz et al. https://doi.org/10.1002/ird.2745
218. Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment D. Bui et al. https://doi.org/10.1016/j.catena.2019.104426
219. An inventory-driven rock glacier status model (intact vs. relict) for South Tyrol, Eastern Italian Alps C. Kofler et al. https://doi.org/10.1016/j.geomorph.2019.106887
220. Heavy Rainfall Triggering Shallow Landslides: A Susceptibility Assessment by a GIS-Approach in a Ligurian Apennine Catchment (Italy) A. Roccati et al. https://doi.org/10.3390/w11030605
221. Application of machine learning techniques in groundwater potential mapping along the west coast of India P. Prasad et al. https://doi.org/10.1080/15481603.2020.1794104
222. A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping Z. Liang et al. https://doi.org/10.3390/rs13081464
223. The influence of forest cover on landslide occurrence explored with spatio-temporal information E. Schmaltz et al. https://doi.org/10.1016/j.geomorph.2017.04.024
224. Ensemble stacking: a powerful tool for landslide susceptibility assessment – a case study in Anhua County, Hunan Province, China L. Liu et al. https://doi.org/10.1080/10106049.2024.2326005
225. GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran M. Zabihi et al. https://doi.org/10.1007/s12665-016-5424-9
226. A comparative evaluation of supervised machine learning algorithms for township level landslide susceptibility zonation in parts of Indian Himalayas B. Peethambaran et al. https://doi.org/10.1016/j.catena.2020.104751
227. Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scale M. Bordoni et al. https://doi.org/10.1007/s10346-020-01592-3
228. Evaluation of neural network models for landslide susceptibility assessment Y. Yi et al. https://doi.org/10.1080/17538947.2022.2062467
229. Landslide Susceptibility Assessment Using Recurrent Neural Network (RNN)—A Case of Chabahar and Konarak in Iran V. Isazade et al. https://doi.org/10.1007/s40098-025-01220-8
230. Comparison of landslide susceptibility models and their robustness analysis: a case study from the NW Himalayas, Pakistan N. Ikram et al. https://doi.org/10.1080/10106049.2021.2017010
231. How does forest structure affect root reinforcement and susceptibility to shallow landslides? C. Moos et al. https://doi.org/10.1002/esp.3887
232. Geoenvironmental conditioning of landsliding in river valleys of lowland regions and its significance in landslide susceptibility assessment: A case study in the Lower Vistula Valley, Northern Poland D. Grabowski et al. https://doi.org/10.1016/j.geomorph.2022.108490
233. Comparison of Novel Hybrid and Benchmark Machine Learning Algorithms to Predict Groundwater Potentiality: Case of a Drought-Prone Region of Medjerda Basin, Northern Tunisia F. Trabelsi et al. https://doi.org/10.3390/rs15010152
234. Global research trends in seismic landslide: A bibliometric analysis M. Yang et al. https://doi.org/10.1016/j.eqrea.2024.100329
235. Integration of FuzzyAHP and machine learning algorithms for climate-driven gully erosion susceptibility mapping: predicting future trends in the eastern lateritic region, West Bengal, India C. Singha et al. https://doi.org/10.1007/s12303-024-0045-x
236. Landslide susceptibility zonation mapping using frequency ratio and fuzzy gamma operator models in part of NH-39, Manipur, India G. Balamurugan et al. https://doi.org/10.1007/s11069-016-2434-6
237. Improving generalization performance of landslide susceptibility model considering spatial heterogeneity by using the geomorphic label-based LightGBM D. Sun et al. https://doi.org/10.1007/s10064-024-03859-8
238. Assessment of landslide susceptibility using machine learning classifiers in Ziz upper watershed, SE Morocco M. Manaouch et al. https://doi.org/10.1080/02723646.2023.2250174
239. A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests Z. Shirvani https://doi.org/10.3390/rs12030434
240. A novel predictive framework for water quality assessment based on socio-economic indicators and water leaving reflectance H. Chen & A. Yunus https://doi.org/10.1016/j.gsd.2025.101405
241. Use of LiDAR-derived DEM and a stream length-gradient index approach to investigation of landslides in Zagros Mountains, Iran S. Pirasteh et al. https://doi.org/10.1080/10106049.2017.1316779
242. An improved information quantity method for non-landslide selection to enhance landslide susceptibility evaluation: a case study in Yongfeng, South China S. Zhai et al. https://doi.org/10.1007/s11069-025-07261-9
243. Evaluation of performance of different machine learning techniques for mapping landslide susceptibility associated with extreme rainfall events in southern Brazil L. Rademann et al. https://doi.org/10.1007/s11069-025-07694-2
244. Development of a New Stacking Model to Evaluate the Strength Parameters of Concrete Samples in Laboratory J. Huang et al. https://doi.org/10.1007/s40996-022-00912-y
245. Determining the Geotechnical Slope Failure Factors via Ensemble and Individual Machine Learning Techniques: A Case Study in Mandi, India N. Mali et al. https://doi.org/10.3389/feart.2021.701837
246. Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan J. Dou et al. https://doi.org/10.1371/journal.pone.0133262
247. Landslide Hazard Prediction Based on UAV Remote Sensing and Discrete Element Model Simulation—Case from the Zhuangguoyu Landslide in Northern China G. Li et al. https://doi.org/10.3390/rs16203887
248. Investigation of the influence of nonoccurrence sampling on landslide susceptibility assessment using Artificial Neural Networks L. Lucchese et al. https://doi.org/10.1016/j.catena.2020.105067
249. Landslide Hazard and Exposure Modelling in Data‐Poor Regions: The Example of the Rohingya Refugee Camps in Bangladesh R. Emberson et al. https://doi.org/10.1029/2020EF001666
250. On the estimation of landslide intensity, hazard and density via data-driven models M. Di Napoli et al. https://doi.org/10.1007/s11069-023-06153-0
251. Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling A. Dahal & L. Lombardo https://doi.org/10.1016/j.cageo.2023.105364
252. Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam H. Nguyen et al. https://doi.org/10.1002/gj.4885
253. Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines T. Carvajal et al. https://doi.org/10.1186/s12879-018-3066-0
254. Assessing the importance of conditioning factor selection in landslide susceptibility for the province of Belluno (region of Veneto, northeastern Italy) S. Meena et al. https://doi.org/10.5194/nhess-22-1395-2022
255. Spatiotemporal landslide susceptibility mapping using machine learning models: A case study from district Hattian Bala, NW Himalaya, Pakistan A. Hammad Khaliq et al. https://doi.org/10.1016/j.asej.2022.101907
256. Role of landslide sampling strategies in susceptibility modelling: types, comparison and mechanism J. Thanveer et al. https://doi.org/10.1007/s10064-024-03851-2
257. Hybrid ensemble machine learning approaches for landslide susceptibility mapping using different sampling ratios at East Sikkim Himalayan, India S. Saha et al. https://doi.org/10.1016/j.asr.2021.05.018
258. Deadly disasters in southeastern South America: flash floods and landslides of February 2022 in Petrópolis, Rio de Janeiro E. Alcântara et al. https://doi.org/10.5194/nhess-23-1157-2023
259. Exploring aspects affecting the predicted capacity of landslide susceptibility based on machine learning technology Q. Liu & A. Tang https://doi.org/10.1080/10106049.2022.2088863
260. Susceptibility assessment of soil-water hazard chain on a small catchment in gully region of Loess Plateau: Implications for artificially-induced mountaintop removal filling valley and geoheritage H. Shu & F. Zhang https://doi.org/10.1016/j.geomorph.2023.108949
261. Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models M. Sinčić et al. https://doi.org/10.5194/nhess-25-183-2025
262. Remote sensing as tool for development of landslide databases: The case of the Messina Province (Italy) geodatabase A. Ciampalini et al. https://doi.org/10.1016/j.geomorph.2015.01.029
263. The influence of spatial patterns in rainfall on shallow landslides H. Smith et al. https://doi.org/10.1016/j.geomorph.2023.108795
264. Shifting from traditional landslide occurrence modeling to scenario estimation with a “glass-box” machine learning F. Caleca et al. https://doi.org/10.1016/j.scitotenv.2024.175277
265. A comparative study on spatial patterns and dominant factors of landslide susceptibility under geomorphological differentiation M. Pan et al. https://doi.org/10.1080/19475705.2026.2635669
266. Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms A. Gayen et al. https://doi.org/10.1016/j.scitotenv.2019.02.436
267. Determination of GIS-Based Landslide Susceptibility and Ground Dynamics with Geophysical Measurements and Machine Learning Algorithms H. Dindar & Ç. Alevkayalı https://doi.org/10.1007/s40891-023-00471-w
268. Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran A. Jamali https://doi.org/10.1007/s42452-019-1527-8
269. Multivariate geospatial data integration and machine learning for landslide susceptibility mapping in the basaltic terrain of Savitri River Basin, Western Ghat, India A. Baile et al. https://doi.org/10.1007/s11069-025-07853-5
270. Landslide susceptibility assessment along the Karakoram highway, Gilgit Baltistan, Pakistan: A comparative study between ensemble and neighbor-based machine learning algorithms F. Abbas et al. https://doi.org/10.1016/j.srs.2024.100132
271. A hybrid model to overcome landslide inventory incompleteness issue for landslide susceptibility prediction J. Tan et al. https://doi.org/10.1080/10106049.2024.2322066
272. Generating a Landslide Susceptibility Map Using Integrated Meta-Heuristic Optimization and Machine Learning Models T. Bostan https://doi.org/10.3390/su16219396
273. Landslide susceptibility mapping using an integration of different statistical models for the 2015 Nepal earthquake in Tibet S. Huang & L. Chen https://doi.org/10.1080/19475705.2024.2396908
274. Comparison of tree-based ensemble learning algorithms for landslide susceptibility mapping in Murgul (Artvin), Turkey Z. Usta et al. https://doi.org/10.1007/s12145-024-01259-w
275. Landslide susceptibility assessment along the Red Sea Coast in Egypt, based on multi-criteria spatial analysis and GIS techniques M. Rashwan et al. https://doi.org/10.1016/j.sciaf.2024.e02116
276. Assessment of rainfall-induced landslide susceptibility in Artvin, Turkey using machine learning techniques H. Akinci https://doi.org/10.1016/j.jafrearsci.2022.104535
277. Quantifying human risk from rainfall‐induced landslides across different return periods: A case study of Ba To, Quang Ngai, Vietnam B. Nguyen & T. Nguyen https://doi.org/10.1002/esp.70210
278. Landslide susceptibility mapping based on the reliability of landslide and non-landslide sample H. Hong et al. https://doi.org/10.1016/j.eswa.2023.122933
279. Landslide damming hazard susceptibility maps: a new GIS-based procedure for risk management C. Tacconi Stefanelli et al. https://doi.org/10.1007/s10346-020-01395-6
280. Scaling land-surface variables for landslide detection F. Sîrbu et al. https://doi.org/10.1186/s40645-019-0290-1
281. A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping W. Chen et al. https://doi.org/10.1007/s12517-015-2150-7
282. Reducing Local Correlations Among Causal Factor Classifications as a Strategy to Improve Landslide Susceptibility Mapping T. Xiao et al. https://doi.org/10.3389/feart.2021.781674
283. Landslide Susceptibility Mapping of Urban Areas: Logistic Regression and Sensitivity Analysis applied to Quito, Ecuador F. Puente-Sotomayor et al. https://doi.org/10.1186/s40677-021-00184-0
284. Uncertainty analysis of non-landslide sample selection in landslide susceptibility prediction using slope unit-based machine learning models Z. Chang et al. https://doi.org/10.1016/j.gr.2023.02.007
285. Preparing a map of the sensitivity of the lands of Ilam province to dust production using data mining models S. Pourhashemi https://doi.org/10.61186/jeer.14.3.83
286. Influence of sampling design on landslide susceptibility modeling in lithologically heterogeneous areas A. Dornik et al. https://doi.org/10.1038/s41598-022-06257-w
287. Machine learning strategies for multiannual rainfall prediction and drought early warning: insights from Ceará, Brazil L. Rolim & F. de Souza Filho https://doi.org/10.1007/s11069-024-07083-1
288. The influence of the inventory on the determination of the rainfall-induced shallow landslides susceptibility using generalized additive models M. Bordoni et al. https://doi.org/10.1016/j.catena.2020.104630
289. Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya I. Chowdhuri et al. https://doi.org/10.1007/s11069-021-04601-3
290. Intrinsic Environmental Vulnerability as Shallow Landslide Susceptibility in Environmental Impact Assessment L. Turconi et al. https://doi.org/10.3390/su11226285
291. How can landslide risk maps be validated? Potential solutions with open-source databases F. Caleca et al. https://doi.org/10.3389/feart.2022.998885
292. Susceptibility Assessment for Landslide Initiated along Power Transmission Lines S. Liu et al. https://doi.org/10.3390/rs13245068
293. Landslide susceptibility mapping using Genetic Algorithm for the Rule Set Production (GARP) model F. Adineh et al. https://doi.org/10.1007/s11629-018-4833-5
294. Preparing first-time slope failures hazard maps: from pixel-based to slope unit-based G. Domènech et al. https://doi.org/10.1007/s10346-019-01279-4
295. Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh A. Islam et al. https://doi.org/10.1007/s11356-021-12806-z
296. DIGSFNet: Deformation-Integrity-Guided Symmetric Fusion Network for High-Risk Landslide Extraction from Multi-Source Remote Sensing Images Z. Ni et al. https://doi.org/10.3390/rs18162692
297. Comparing methods of landslide data acquisition and susceptibility modelling: Examples from New Zealand H. Smith et al. https://doi.org/10.1016/j.geomorph.2021.107660
298. Predicting Landslides Susceptible Zones in the Lesser Himalayas by Ensemble of Per Pixel and Object-Based Models U. Sur et al. https://doi.org/10.3390/rs14081953
299. Landslide Prediction Validation in Western North Carolina After Hurricane Helene S. Lin et al. https://doi.org/10.3390/geotechnics4040064
300. Wildfire Susceptibility Assessment in Southern China: A Comparison of Multiple Methods Y. Cao et al. https://doi.org/10.1007/s13753-017-0129-6
301. Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China Y. Xing et al. https://doi.org/10.3389/feart.2021.722491
302. Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park L. Gigović et al. https://doi.org/10.3390/f10050408
303. Evaluation of geological hazard susceptibility based on the multi-kernel density information method Y. Li et al. https://doi.org/10.1038/s41598-025-91713-6
304. Advancing Landslide Susceptibility Mapping in the Medea Region Using a Hybrid Metaheuristic ANFIS Approach F. Debiche et al. https://doi.org/10.3390/land13060889
305. Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models S. Elmahdy et al. https://doi.org/10.3389/fenvs.2020.00102
306. Landslide susceptibility assessment by Dempster–Shafer and Index of Entropy models, Sarkhoun basin, Southwestern Iran K. Shirani et al. https://doi.org/10.1007/s11069-018-3356-2
307. Chamoli flash-flood mapping and evaluation with a supervised classifier and NDWI thresholding using Sentinel-2 optical data in Google earth engine S. Singh & M. Kansal https://doi.org/10.1007/s12145-022-00786-8
308. Assessment of landslide susceptibility zonation using frequency ratio and statistical index: a case study of Al-Fawar basin, Tartous, Syria H. Abdo https://doi.org/10.1007/s13762-021-03322-1
309. A data-driven method for the estimation of shallow landslide runout A. Giarola et al. https://doi.org/10.1016/j.catena.2023.107573
310. Landslide susceptibility assessment through multi-model stacking and meta-learning in Poyang County, China Y. Song et al. https://doi.org/10.1080/19475705.2024.2354499
311. Exploring the spatial distribution patterns of loess landslides and identifying the key driving factors in typical regions of the upper Yellow River, China H. Shu et al. https://doi.org/10.1080/19475705.2026.2636709
312. Influence of buffer distance on environmental geological hazard susceptibility assessment Z. Wang et al. https://doi.org/10.1007/s11356-023-31739-3
313. Exploring uncertainty analysis in GIS-based Landslide susceptibility mapping models using machine learning in the Darjeeling Himalayas S. Dey et al. https://doi.org/10.1007/s12145-024-01561-7
314. Integration of rotation forest and multiboost ensemble methods with forest by penalizing attributes for spatial prediction of landslide susceptible areas T. Bien et al. https://doi.org/10.1007/s00477-023-02521-1
315. Spatial Prediction of Landslides Using Hybrid Integration of Artificial Intelligence Algorithms with Frequency Ratio and Index of Entropy in Nanzheng County, China W. Chen et al. https://doi.org/10.3390/app10010029
316. Landslide susceptibility assessment in a lesser Himalayan road corridor (India) applying fuzzy AHP technique and earth-observation data U. Sur et al. https://doi.org/10.1080/19475705.2020.1836038
317. Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model Z. Yaseen et al. https://doi.org/10.3390/su12041514
318. Landslide susceptibility mapping in the Bokoya Massif, Northern Morocco: A geospatial and multi-factor analysis using the analytic hierarchy process (AHP) M. Ait Omar et al. https://doi.org/10.1016/j.sciaf.2025.e02980
319. Deep learning-based landslide detection using Sentinel-1 SAR imagery in the Chilean Patagonia F. Parra et al. https://doi.org/10.1007/s11069-026-08395-0
320. A modified frequency ratio method for landslide susceptibility assessment L. Li et al. https://doi.org/10.1007/s10346-016-0771-x
321. An applied statistical method to identify desertification indicators in northeastern Iran M. Sarparast et al. https://doi.org/10.1186/s40677-018-0095-3
322. LCZ scheme for assessing Urban Heat Island intensity in a complex urban area (Beirut, Lebanon) N. Badaro-Saliba et al. https://doi.org/10.1016/j.uclim.2021.100846
323. Regional-scale controls on the spatial activity of rockfalls (Turtmann Valley, Swiss Alps) — A multivariate modeling approach K. Messenzehl et al. https://doi.org/10.1016/j.geomorph.2016.01.008
324. The propagation of inventory-based positional errors into statistical landslide susceptibility models S. Steger et al. https://doi.org/10.5194/nhess-16-2729-2016
325. Suitable Site Selection of Fog Water Harvesting Based-On RS Data in Upstream of Vazrud Watershed in Iran K. solaimani & F. Shokrian https://doi.org/10.52547/jwmr.11.21.249
326. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance A. Merghadi et al. https://doi.org/10.1016/j.earscirev.2020.103225
327. Statistical Analysis of the Potential of Landslides Induced by Combination between Rainfall and Earthquakes C. Tseng et al. https://doi.org/10.3390/w14223691
328. Improving Landslide Detection on SAR Data Through Deep Learning L. Nava et al. https://doi.org/10.1109/LGRS.2021.3127073
329. Probability mapping of debris flows triggered by multiple mechanisms in the Himalayas B. Zhou et al. https://doi.org/10.1016/j.ijdrr.2025.105444
330. Landslide detection by deep learning of non-nadiral and crowdsourced optical images F. Catani https://doi.org/10.1007/s10346-020-01513-4
331. Consideration of spatial heterogeneity in landslide susceptibility mapping using geographical random forest model R. Quevedo et al. https://doi.org/10.1080/10106049.2021.1996637
332. Mapping water erosion susceptibility in Northeastern Algeria using two machine learning models K. Zerroug et al. https://doi.org/10.1007/s11629-025-9727-8
333. Production and Analysis of a Landslide Susceptibility Map Covering Entire China G. Zhang et al. https://doi.org/10.3390/rs17091615
334. Performance Evaluation of Machine Learning Algorithms in Change Detection and Change Prediction of a Watershed’s Land Use and Land Cover M. Mousavinezhad et al. https://doi.org/10.1007/s41742-023-00518-w
335. Mapping at-risk transportation infrastructure assets using statistical and machine learning methods R. Salunke & S. Khan https://doi.org/10.1038/s41598-025-18440-w
336. Spatial assessment of termites interaction with groundwater potential conditioning parameters in Keffi, Nigeria J. Ahmed II & B. Pradhan https://doi.org/10.1016/j.jhydrol.2019.124012
337. Classification of Urban Land Subsidence Types in Fuzhou from Time-Series InSAR Using FFT-Based Filtering and Ensemble Learning Z. Zhao et al. https://doi.org/10.3390/rs18162778
338. Feature Engineering of Geohazard Susceptibility Analysis Based on the Random Forest Algorithm: Taking Tianshui City, Gansu Province, as an Example X. Ling et al. https://doi.org/10.3390/rs14225658
339. Regional Landslide Identification Based on Susceptibility Analysis and Change Detection A. Si et al. https://doi.org/10.3390/ijgi7100394
340. Introducing stacking machine learning approaches for the prediction of rock deformation M. Koopialipoor et al. https://doi.org/10.1016/j.trgeo.2022.100756
341. Landscape Characteristics in Relation to Ecosystem Services Supply: The Case of a Mediterranean Forest on the Island of Cyprus G. Kefalas et al. https://doi.org/10.3390/f14071286
342. Optimal statistical method selection for landslide susceptibility assessment and its scale effect Y. Yang et al. https://doi.org/10.3389/feart.2024.1464775
343. Landslide Susceptibility Level Mapping in Kozhikode, Kerala, Using Machine Learning-Based Random Forest, Remote Sensing, and GIS Techniques P. Badapalli et al. https://doi.org/10.3390/land14071453
344. Developing drought impact functions for drought risk management S. Bachmair et al. https://doi.org/10.5194/nhess-17-1947-2017
345. Multi-Resolution Landslide Susceptibility Analysis Using a DEM and Random Forest U. Paudel et al. https://doi.org/10.4236/ijg.2016.75056
346. Forest fire susceptibility mapping with sensitivity and uncertainty analysis using machine learning and deep learning algorithms M. Rihan et al. https://doi.org/10.1016/j.asr.2023.03.026
347. An information quantity and machine learning integrated model for landslide susceptibility mapping in Jiuzhaigou, China Y. Yang et al. https://doi.org/10.1007/s11069-024-06602-4
348. Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques K. Chang et al. https://doi.org/10.1038/s41598-019-48773-2
349. Dust source susceptibility mapping based on remote sensing and machine learning techniques R. Jafari et al. https://doi.org/10.1016/j.ecoinf.2022.101872
350. Prediction of forest fire susceptibility applying machine and deep learning algorithms for conservation priorities of forest resources S. Saha et al. https://doi.org/10.1016/j.rsase.2022.100917
351. Probabilistic assessment of a road network subjected to rainfall-induced landslides C. Wu et al. https://doi.org/10.1016/j.ress.2025.111409
352. Statistical knowledge-guided multi-classification machine learning modeling scheme for landslide susceptibility levels F. Zhang et al. https://doi.org/10.1016/j.enggeo.2025.108458
353. Modelling human health vulnerability using different machine learning algorithms in stone quarrying and crushing areas of Dwarka river Basin, Eastern India I. Mandal & S. Pal https://doi.org/10.1016/j.asr.2020.05.032
354. Optimal Mapping of Soil Erodibility Factor (K) Using Machine Learning Models in a Semi-arid Watershed M. Ghavami et al. https://doi.org/10.1007/s41748-024-00553-3
355. Exploring multiscale relationships between environmental characteristics and recreational trail-based activities in urban natural areas: A regional study leveraging user-generated big data and machine learning P. Chang et al. https://doi.org/10.1016/j.ufug.2025.128869
356. Machine-Learning Applications in Geosciences: Comparison of Different Algorithms and Vegetation Classes’ Importance Ranking in Wildfire Susceptibility A. Trucchia et al. https://doi.org/10.3390/geosciences12110424
357. Flood susceptibility assessment using extreme gradient boosting (EGB), Iran S. Mirzaei et al. https://doi.org/10.1007/s12145-020-00530-0
358. Landslide susceptibility modelling using different advanced decision trees methods B. Thai Pham et al. https://doi.org/10.1080/10286608.2019.1568418
359. Capability assessment of machine learning classifiers for spatial landslide susceptibility mapping in Malappuram district, Kerala, India S. Giri et al. https://doi.org/10.1080/02723646.2025.2552290
360. Mapping landslide susceptibility using data-driven methods J. Zêzere et al. https://doi.org/10.1016/j.scitotenv.2017.02.188
361. Integration of rainfall thresholds and susceptibility maps in the Emilia Romagna (Italy) regional-scale landslide warning system S. Segoni et al. https://doi.org/10.1007/s10346-014-0502-0
362. Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier B. Pham et al. https://doi.org/10.1007/s12524-018-0791-1
363. Analysis of Landslide Susceptibility Using Deep Neural Network C. Song et al. https://doi.org/10.9798/KOSHAM.2021.21.3.141
364. A step beyond landslide susceptibility maps: a simple method to investigate and explain the different outcomes obtained by different approaches T. Xiao et al. https://doi.org/10.1007/s10346-019-01299-0
365. Comprehensive landslide susceptibility map of Central Asia A. Rosi et al. https://doi.org/10.5194/nhess-23-2229-2023
366. Threats of climate and land use change on future flood susceptibility P. Roy et al. https://doi.org/10.1016/j.jclepro.2020.122757
367. Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors Z. Chang et al. https://doi.org/10.1016/j.jrmge.2022.07.009
368. Landslide spatial probability prediction: a comparative assessment of naïve Bayes, ensemble learning, and deep learning approaches B. Nguyen & Y. Kim https://doi.org/10.1007/s10064-021-02194-6
369. A bibliometric analysis of the landslide susceptibility research (1999–2021) L. Liu et al. https://doi.org/10.1080/10106049.2022.2087753
370. Debris flow susceptibility assessment based on information value and machine learning coupling method: from the perspective of sustainable development J. Cao et al. https://doi.org/10.1007/s11356-023-28575-w
371. Optimization and validation of air impingement cleaning parameters for removing nonfat dry milk powder from stainless steel surfaces by machine learning Z. Liu et al. https://doi.org/10.3168/jds.2025-27601
372. Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale S. Qin et al. https://doi.org/10.1007/s11069-022-05487-5
373. Identifying sources of dust aerosol using a new framework based on remote sensing and modelling O. Rahmati et al. https://doi.org/10.1016/j.scitotenv.2020.139508
374. Prediction of Landslide Susceptibility in the Karakorum under the Context of Climate Change Y. Pei et al. https://doi.org/10.3390/app14188562
375. Evaluating the use of a coordinate-based tensor-product for addressing spatial autocorrelation in shallow landslide susceptibility modelling L. Pompili et al. https://doi.org/10.1007/s00477-026-03283-2
376. Comprehensive analysis of landslide stability and related countermeasures: a case study of the Lanmuxi landslide in China Z. Han et al. https://doi.org/10.1038/s41598-019-48934-3
377. Can Terrestrial Water Storage Dynamics be Estimated From Climate Anomalies? W. Jing et al. https://doi.org/10.1029/2019EA000959
378. Advanced machine learning techniques for enhanced landslide susceptibility mapping: Integrating geotechnical parameters in the case of Southwestern Cyprus P. Tzampoglou et al. https://doi.org/10.1007/s12145-025-01761-9
379. Shallow landslides susceptibility assessment in different environments M. Persichillo et al. https://doi.org/10.1080/19475705.2016.1265011
380. Landslide susceptibility mapping: improvements in variable weights estimation through machine learning algorithms—a case study of upper Indus River Basin, Pakistan I. Imtiaz et al. https://doi.org/10.1007/s12665-022-10233-y
381. Landslide susceptibility evaluation and interpretability analysis of typical loess areas based on deep learning L. Chang et al. https://doi.org/10.1016/j.nhres.2023.02.005
382. Field-based landslide susceptibility assessment in a data-scarce environment: the populated areas of the Rwenzori Mountains L. Jacobs et al. https://doi.org/10.5194/nhess-18-105-2018
383. Machine-learning-constrained projection of bivariate hydrological drought magnitudes and socioeconomic risks over China R. Liu et al. https://doi.org/10.5194/hess-28-3305-2024
384. Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques A. Arabameri et al. https://doi.org/10.1016/j.ejrh.2021.100848
385. Input Modality Ablation for Sustainable Landslide Hazard Management Using U-Net: Fused DEM–Optical vs. Spectral vs. Terrain Representations in a Small-Sample Pilot Study W. Chen & F. Tsai https://doi.org/10.3390/su18136649
386. Mapping landslide susceptibility and types using Random Forest K. Taalab et al. https://doi.org/10.1080/20964471.2018.1472392
387. Landslide risk assessment considering socionatural factors: methodology and application to Cubatão municipality, São Paulo, Brazil P. Hader et al. https://doi.org/10.1007/s11069-021-04991-4
388. A novel physical process-ensemble learning model framework with residual error decomposition to upskill daily runoff prediction Y. Kang et al. https://doi.org/10.1016/j.jhydrol.2024.132565
389. Rapidly Evolving Controls of Landslides After a Strong Earthquake and Implications for Hazard Assessments X. Fan et al. https://doi.org/10.1029/2020GL090509
390. GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China P. Wang et al. https://doi.org/10.3390/w10081019
391. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques Z. Pourtaghi et al. https://doi.org/10.1016/j.ecolind.2015.12.030
392. A study on the use of planarity for quick identification of potential landslide hazard M. Baek & T. Kim https://doi.org/10.5194/nhess-15-997-2015
393. Landslide susceptibility mapping using an ensemble model of Bagging scheme and random subspace–based naïve Bayes tree in Zigui County of the Three Gorges Reservoir Area, China X. Hu et al. https://doi.org/10.1007/s10064-021-02275-6
394. Integrating multidisciplinary approach to decipher the peculier incidence of burst of new water source and perpetual problem of ground subsidence in Joshimath, Uttarakhand H. Bahuguna et al. https://doi.org/10.1007/s11069-025-07737-8
395. النمذجة المكانية لاستقرارية المنحدرات الارضية في حوض نهر الزاب الاسفل م. غثوان https://doi.org/10.25130/jtuh.31.11.2024.10
396. Relationship between continuous or discontinuous of controlling factors and landslide susceptibility in the high-cold mountainous areas, China H. Shu et al. https://doi.org/10.1016/j.ecolind.2025.113313
397. The Role of Collecting Data on Various Site Conditions Through Satellite Remote Sensing Technology and Field Surveys in Predicting the Landslide Travel Distance: A Case Study of the 2022 Petrópolis Disaster in Brazil T. dos Santos & T. Uchida https://doi.org/10.3390/rs17193337
398. Bias-reduced landslide susceptibility mapping in data-scarce regions via multi-source inventory integration Y. Xiong et al. https://doi.org/10.1080/19475705.2026.2717802
399. Spatial Pattern Analysis of Landslide Conditioning Factors in Jecheon Area Using SHAP Method J. Choi et al. https://doi.org/10.9719/EEG.2025.58.1.81
400. Quantitative landslide risk assessment for Italy F. Caleca et al. https://doi.org/10.1007/s10346-025-02590-z
401. Landslide susceptibility modelling based on AHC-OLID clustering algorithm Y. Mao et al. https://doi.org/10.1016/j.asr.2021.03.014
402. Landslide susceptibility assessment of the Wanzhou district: Merging landslide susceptibility modelling (LSM) with InSAR-derived ground deformation map C. Zhou et al. https://doi.org/10.1016/j.jag.2025.104365
403. Data-driven methods to improve baseflow prediction of a regional groundwater model T. Xu & A. Valocchi https://doi.org/10.1016/j.cageo.2015.05.016
404. Landslide probability mapping by considering fuzzy numerical risk factor (FNRF) and landscape change for road corridor of Uttarakhand, India U. Sur et al. https://doi.org/10.1007/s10668-021-01226-1
405. Landslide susceptibility of the Prato–Pistoia–Lucca provinces, Tuscany, Italy S. Segoni et al. https://doi.org/10.1080/17445647.2016.1233463
406. Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping S. Naghibi et al. https://doi.org/10.1007/s11269-017-1660-3
407. Application of Bayesian Hyperparameter Optimized Random Forest and XGBoost Model for Landslide Susceptibility Mapping S. Wang et al. https://doi.org/10.3389/feart.2021.712240
408. Spatial mapping of hydrologic soil groups using machine learning in the Mediterranean region E. Faouzi et al. https://doi.org/10.1016/j.catena.2023.107364
409. Exploring different approaches for landslide susceptibility zonation mapping in Manipur: a comparative study of AHP, FR, machine learning, and deep learning models A. Kshetrimayum et al. https://doi.org/10.1080/14498596.2024.2368156
410. Assessment of environmental geological disaster susceptibility under a multimodel comparison to aid in the sustainable development of the regional economy C. Wang et al. https://doi.org/10.1007/s11356-022-22649-x
411. Method for prediction of landslide movements based on random forests M. Krkač et al. https://doi.org/10.1007/s10346-016-0761-z
412. A Tool for Classification and Regression Using Random Forest Methodology: Applications to Landslide Susceptibility Mapping and Soil Thickness Modeling D. Lagomarsino et al. https://doi.org/10.1007/s10666-016-9538-y
413. Artificial neural networks applied to landslide susceptibility: The effect of sampling areas on model capacity for generalization and extrapolation S. Gameiro et al. https://doi.org/10.1016/j.apgeog.2021.102598
414. Water‐Inrush Risk Assessment of Roof‐Confined Aquifer Strata Based on FAHP–RF Model in the Arid–Semiarid Ecotone: A Case Study of Zhuanlongwan Coal Mine S. Xue et al. https://doi.org/10.1155/gfl/5360550
415. Discussion on the tree-based machine learning model in the study of landslide susceptibility Q. Liu et al. https://doi.org/10.1007/s11069-022-05329-4
416. A Combined Method for Preparation of Landslide Susceptibility Map in Izmir (Türkiye) C. KINCAL & H. KAYHAN https://doi.org/10.3390/app12189029
417. Optimization of SVR and CatBoost models using metaheuristic algorithms to assess landslide susceptibility R. Ajin et al. https://doi.org/10.1038/s41598-024-72663-x
418. A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping S. Naghibi & H. Pourghasemi https://doi.org/10.1007/s11269-015-1114-8
419. Using data-driven algorithms for semi-automated geomorphological mapping E. Giaccone et al. https://doi.org/10.1007/s00477-021-02062-5
420. Machine learning for landslides prevention: a survey Z. Ma et al. https://doi.org/10.1007/s00521-020-05529-8
421. Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach G. Grekousis et al. https://doi.org/10.1016/j.healthplace.2022.102744
422. Evaluating key environmental variables in dust occurrence through artificial intelligence methods M. Pourhanifeh et al. https://doi.org/10.1016/j.ecoinf.2026.103653
423. Landslide Risk Associated with Glacier Tourism in the Mt. Everest Region (Sagarmatha National Park), High-Mountain Nepal L. Sapkota et al. https://doi.org/10.3390/earth7020043
424. Developing comprehensive geocomputation tools for landslide susceptibility mapping: LSM tool pack E. Sahin et al. https://doi.org/10.1016/j.cageo.2020.104592
425. A Hybrid Variable Weight Theory Approach of Hierarchical Analysis and Multi-Layer Perceptron for Landslide Susceptibility Evaluation: A Case Study in Luanchuan County, China M. Li et al. https://doi.org/10.3390/su15031908
426. Mapping China’s Changing Gross Domestic Product Distribution Using Remotely Sensed and Point-of-Interest Data with Geographical Random Forest Model F. Deng et al. https://doi.org/10.3390/su15108062
427. Effect of spatial resolution and data splitting on landslide susceptibility mapping using different machine learning algorithms M. Abraham et al. https://doi.org/10.1080/19475705.2021.2011791
428. A landslide susceptibility assessment method considering the similarity of geographic environments based on graph neural network Q. Zhang et al. https://doi.org/10.1016/j.gr.2024.04.013
429. Mapping soil suitability using phenological information derived from MODIS time series data in a semi-arid region: A case study of Khouribga, Morocco M. Ismaili et al. https://doi.org/10.1016/j.heliyon.2024.e24101
430. 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. https://doi.org/10.3390/rs12111826
431. Spatial Prediction of Landslide Susceptibility Based on GIS and Discriminant Functions G. Wang et al. https://doi.org/10.3390/ijgi9030144
432. National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data Q. Lin et al. https://doi.org/10.1016/j.gsf.2021.101248
433. Leveraging Machine Learning Methods for Predicting Employee Turnover Within the Framework of Human Resources Analytics Z. Taner et al. https://doi.org/10.38016/jista.1440879
434. GIS-based for prediction and prevention of environmental geological disaster susceptibility: From a perspective of sustainable development X. Wang et al. https://doi.org/10.1016/j.ecoenv.2021.112881
435. Modeling Shallow Landslide Susceptibility and Assessment of the Relative Importance of Predisposing Factors, through a GIS-Based Statistical Analysis M. Conforti & F. Ietto https://doi.org/10.3390/geosciences11080333
436. Predictive Modelling of Landslide Susceptibility in the Western Carpathian Flysch Zone M. Barančoková et al. https://doi.org/10.3390/land10121370
437. Landslide susceptibility assessment in complex geological settings: sensitivity to geological information and insights on its parameterization S. Segoni et al. https://doi.org/10.1007/s10346-019-01340-2
438. Coupling Downscaling and Calibrating Methods for Generating High-Quality Precipitation Data with Multisource Satellite Data in the Yellow River Basin H. Yang et al. https://doi.org/10.3390/rs16081318
439. Integrating principal component analysis with statistically-based models for analysis of causal factors and landslide susceptibility mapping: A comparative study from the loess plateau area in Shanxi (China) Y. Tang et al. https://doi.org/10.1016/j.jclepro.2020.124159
440. Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling P. Nguyen et al. https://doi.org/10.3390/su12072622
441. A Comprehensive Comparison of Stable and Unstable Area Sampling Strategies in Large-Scale Landslide Susceptibility Models Using Machine Learning Methods M. Sinčić et al. https://doi.org/10.3390/rs16162923
442. Landslide susceptibility mapping using Frequency Ratio, Random Forest, and XGBoost Models along NH-7 between Nandprayag and Vishnuprayag . Sidra et al. https://doi.org/10.1007/s44288-026-00683-3
443. Dynamic landslide susceptibility mapping based on the PS-InSAR deformation intensity B. Jin et al. https://doi.org/10.1007/s11356-023-31688-x
444. Investigation of influencing factors for valley deformation of high arch dam using machine learning H. Shi et al. https://doi.org/10.1080/19648189.2020.1763842
445. Enhancing wind erosion hazard assessment: a novel framework combining integrated land susceptibility to wind erosion (ILSWE) index and machine learning algorithms (case study: Saravan area, Southeast Iran) M. Mohammadi et al. https://doi.org/10.1016/j.aeolia.2025.100995
446. Using the rotation and random forest models of ensemble learning to predict landslide susceptibility L. Zhao et al. https://doi.org/10.1080/19475705.2020.1803421
447. An ensemble of dynamic rainfall index and machine learning method for spatiotemporal landslide susceptibility modeling T. Ren et al. https://doi.org/10.1007/s10346-023-02152-1
448. Using an ensemble machine learning model to delineate groundwater potential zones in desert fringes of East Esna-Idfu area, Nile valley, Upper Egypt H. Morgan et al. https://doi.org/10.1186/s40562-023-00261-2
449. A Simplified ArcGIS Approach for Landslides Risk Assessment in the Province of Bergamo B. Marana https://doi.org/10.4236/jgis.2017.96044
450. Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards Ö. Ekmekcioğlu & K. Koc https://doi.org/10.1016/j.catena.2022.106379
451. Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning J. Dou et al. https://doi.org/10.1016/j.scitotenv.2020.137320
452. Fault diagnosis in industrial chemical processes using optimal probabilistic neural network Z. Xie et al. https://doi.org/10.1002/cjce.23491
453. 基于AI技术的滑坡易发性制图研究进展 C. Liu et al. https://doi.org/10.3799/dqkx.2024.114
454. Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling – Benefits of exploring landslide data collection effects S. Steger et al. https://doi.org/10.1016/j.scitotenv.2021.145935
455. Multicollinearity and spatial correlation analysis of landslide conditioning factors in Langat River Basin, Selangor S. Selamat et al. https://doi.org/10.1007/s11069-024-06903-8
456. Advanced landslide susceptibility mapping and analysis of driving mechanisms using ensemble machine learning models M. Maashi et al. https://doi.org/10.1016/j.jsames.2024.105272
457. Rainfall-Induced Landslide Hazard Assessment Considering Multi-Temporal-Scale Rainfall Factors in Jiangwan Town, Shaoguan, Guangdong Province, China J. Yang et al. https://doi.org/10.3390/geosciences16090352
458. Landslide susceptibility mapping in the Loess Plateau of northwest China using three data-driven techniques-a case study from middle Yellow River catchment Z. Guo et al. https://doi.org/10.3389/feart.2022.1033085
459. Late Pleistocene dynamics of dust emissions related to westerlies revealed by quantifying loess provenance changes in North Tian Shan, Central Asia Y. Li et al. https://doi.org/10.1016/j.catena.2023.107101
460. Decoding dynamic landslide hazard processes for a massive refugee camp in Bangladesh D. Haque et al. https://doi.org/10.1016/j.envc.2025.101172
461. Assessing the Efficacy of Ensemble Learning Techniques to Slope Stability Classification in Mining Operations M. Mnzool et al. https://doi.org/10.1007/s42461-025-01271-8
462. Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models R. Schlögel et al. https://doi.org/10.1016/j.geomorph.2017.10.018
463. Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia A. Youssef & H. Pourghasemi https://doi.org/10.1016/j.gsf.2020.05.010
464. Novel Ensemble Landslide Predictive Models Based on the Hyperpipes Algorithm: A Case Study in the Nam Dam Commune, Vietnam Q. Tran et al. https://doi.org/10.3390/app10113710
465. A novel dynamic rockfall susceptibility model including precipitation, temperature and snowmelt predictors: a case study in Aosta Valley (northern Italy) G. Bajni et al. https://doi.org/10.1007/s10346-023-02091-x
466. Identifying the essential influencing factors of landslide susceptibility models based on hybrid-optimized machine learning with different grid resolutions: a case of Sino-Pakistani Karakorum Highway J. Wu et al. https://doi.org/10.1007/s11356-023-29234-w
467. Assessing landslide susceptibility using frequency ratio and logistic regression models in the Khudi Watershed S. Hamal et al. https://doi.org/10.1186/s40562-026-00467-0
468. Monthly sodium adsorption ratio forecasting in rivers using a dual interpretable glass-box complementary intelligent system: Hybridization of ensemble TVF-EMD-VMD, Boruta-SHAP, and eXplainable GPR M. Jamei et al. https://doi.org/10.1016/j.eswa.2023.121512
469. Association Rules-Based Multivariate Analysis and Visualization of Spatiotemporal Climate Data F. Wang et al. https://doi.org/10.3390/ijgi7070266
470. Assessing the effectiveness of alternative landslide partitioning in machine learning methods for landslide prediction in the complex Himalayan terrain M. Riaz et al. https://doi.org/10.1177/03091333221113660
471. A GIS- Based Framework for Landslide Susceptibility Mapping Using Bivariate Statistical Models A. Bin Firoz et al. https://doi.org/10.1007/s41742-026-01094-5
472. Mapping of Water-Induced Soil Erosion Using Machine Learning Models: A Case Study of Oum Er Rbia Basin (Morocco) A. Barakat et al. https://doi.org/10.1007/s41748-022-00317-x
473. Combing Random Forest and Least Square Support Vector Regression for Improving Extreme Rainfall Downscaling Q. Pham et al. https://doi.org/10.3390/w11030451
474. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran H. Pourghasemi & N. Kerle https://doi.org/10.1007/s12665-015-4950-1
475. A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS S. Naghibi et al. https://doi.org/10.1007/s00704-016-2022-4
476. Multi-hazard susceptibility mapping based on Convolutional Neural Networks K. Ullah et al. https://doi.org/10.1016/j.gsf.2022.101425
477. Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms S. Band et al. https://doi.org/10.3390/rs12213568
478. Spatial prediction of spring locations in data poor region of Central Himalayas R. Niraula et al. https://doi.org/10.2166/nh.2020.223
479. Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results V. Capecchi et al. https://doi.org/10.5194/nhess-15-75-2015
480. Exploring performance and robustness of shallow landslide susceptibility modeling at regional scale using different training and testing sets M. Conforti et al. https://doi.org/10.1007/s12665-023-10844-z
481. A Heterogeneous Sampling Strategy to Model Earthquake-Triggered Landslides H. Yang et al. https://doi.org/10.1007/s13753-023-00489-8
482. GIS and machine learning methods for landslide susceptibility mapping: an analysis of Al Hoceima, Morocco H. Azour et al. https://doi.org/10.1007/s42990-026-00244-3
483. Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction M. Zaki et al. https://doi.org/10.3390/app13137622
484. GIS-based machine learning models for assessing landslide impact in King County, Washington, USA D. Lu & T. Oguchi https://doi.org/10.1186/s40645-026-00817-8
485. Assessing and mapping landslide susceptibility using different machine learning methods O. Orhan et al. https://doi.org/10.1080/10106049.2020.1837258
486. A Scientometric Analysis of Predicting Methods for Identifying the Environmental Risks Caused by Landslides Y. Zou & C. Zheng https://doi.org/10.3390/app12094333
487. Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan M. Juliev et al. https://doi.org/10.1016/j.scitotenv.2018.10.431
488. Machine Learning and Experimental Identification of Parameters for Discrete Element Method Simulations: Binary Mixtures of Pellets and Lump Ores K. Li et al. https://doi.org/10.1002/srin.202500714
489. Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility P. Lima et al. https://doi.org/10.1007/s11629-021-7254-9
490. Refinement of Landslide Susceptibility Map Using Persistent Scatterer Interferometry in Areas of Intense Mining Activities in the Karst Region of Southwest China C. Shen et al. https://doi.org/10.3390/rs11232821
491. Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping B. Kalantar et al. https://doi.org/10.3390/w11091909
492. Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, Iran Q. Pham et al. https://doi.org/10.1080/19475705.2020.1837968
493. Examining the nonlinear relationship between neighborhood environment and residents' health J. Xu et al. https://doi.org/10.1016/j.cities.2024.105213
494. Intelligent Approach Based on Random Forest for Safety Risk Prediction of Deep Foundation Pit in Subway Stations Y. Zhou et al. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000796
495. Towards evaluating gully erosion volume and erosion rates in the Chambal badlands, Central India R. Raj et al. https://doi.org/10.1002/ldr.4250
496. PSLSA v2.0: An automatic Python package integrating machine learning models for regional landslide susceptibility assessment Z. Guo et al. https://doi.org/10.1016/j.envsoft.2025.106367
497. Application of Machine Learning and Deep Learning Algorithms for Landslide Susceptibility Assessment in Landslide Prone Himalayan Region S. Bhattacharya et al. https://doi.org/10.1007/s41748-024-00530-w
498. Dynamic Earth Observation for Landslide Susceptibility Mapping Using Machine Learning and InSAR-Derived Deformation A. Kerekes et al. https://doi.org/10.3390/app16157842
499. Landslide Geo-Hazard Risk Mapping Using Logistic Regression Modeling in Guixi, Jiangxi, China W. Huangfu et al. https://doi.org/10.3390/su13094830
500. Coupled model for simulation of landslides and debris flows at local scale D. Park et al. https://doi.org/10.1007/s11069-016-2150-2
501. Landslide susceptibility classification using multi hive artificial bee colony programming: A novel symbolic regression framework S. Arslan et al. https://doi.org/10.1016/j.eswa.2025.129324
502. Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy S. Segoni et al. https://doi.org/10.3390/rs16234491
503. Integrating Landslide Typology with Weighted Frequency Ratio Model for Landslide Susceptibility Mapping: A Case Study from Lanzhou City of Northwestern China H. Shu et al. https://doi.org/10.3390/rs13183623
504. Prolonged influence of urbanization on landslide susceptibility T. Rohan et al. https://doi.org/10.1007/s10346-023-02050-6
505. Mapping Susceptibility With Open-Source Tools: A New Plugin for QGIS G. Titti et al. https://doi.org/10.3389/feart.2022.842425
506. Regional landslide susceptibility mapping of earthflows using deep neural networks: A case study in the southern Apennines, Italy A. Doglioni et al. https://doi.org/10.1016/j.enggeo.2026.108753
507. Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping E. Sahin https://doi.org/10.1080/10106049.2020.1831623
508. Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China J. Yao et al. https://doi.org/10.3390/app10165640
509. Evaluating the uncertainty in landslide susceptibility prediction: effect of spatial data variability and evaluation unit choices S. Qin et al. https://doi.org/10.1007/s10064-025-04180-8
510. Technical Note: An operational landslide early warning system at regional scale based on space–time-variable rainfall thresholds S. Segoni et al. https://doi.org/10.5194/nhess-15-853-2015
511. Seismic-induced landslides susceptibility mapping of the NEOM area, northwestern Saudi Arabia using machine learning models B. El-Haddad et al. https://doi.org/10.1007/s12145-025-01864-3
512. Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China C. Zhou et al. https://doi.org/10.1016/j.cageo.2017.11.019
513. Exploration of Glacial Landforms by Object-Based Image Analysis and Spectral Parameters of Digital Elevation Model L. Janowski et al. https://doi.org/10.1109/TGRS.2021.3091771
514. Influence of spatial heterogeneity on landslide susceptibility in the transboundary area of the Himalayas H. Sun et al. https://doi.org/10.1016/j.geomorph.2023.108723
515. The influence of DEM spatial resolution on landslide susceptibility mapping in the Baxie River basin, NW China Z. Chen et al. https://doi.org/10.1007/s11069-020-03899-9
516. A multi-objective optimization framework for mudflow susceptibility mapping in the Yanshan Mountains: Integrating nondominated sorting genetic algorithm-II, random forest, and gradient boosting decision trees Q. Wang et al. https://doi.org/10.1063/5.0276977
517. A spatially explicit database of wind disturbances in European forests over the period 2000–2018 G. Forzieri et al. https://doi.org/10.5194/essd-12-257-2020
518. Random Forests for Landslide Prediction in Tsengwen River Watershed, Central Taiwan Y. Cheng et al. https://doi.org/10.3390/rs13020199
519. An artificial intelligence-based approach to predicting seismic hillslope stability under extreme rainfall events in the vicinity of Wolsong nuclear power plant, South Korea A. Pradhan & Y. Kim https://doi.org/10.1007/s10064-021-02138-0
520. Advances and Challenges in Landslide Prediction and Forecasting H. Tang et al. https://doi.org/10.3724/BNSFC-2025-0023
521. A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS Q. Nguyen et al. https://doi.org/10.3390/su9050813
522. Prediction of groundwater level changes based on machine learning technique in highly groundwater irrigated alluvial aquifers of south-central Punjab, India S. Gupta et al. https://doi.org/10.1016/j.pce.2024.103603
523. Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping: a priority assessment of sub-basins A. Mosavi et al. https://doi.org/10.1080/10106049.2020.1829101
524. Global satellite analysis reveals landslide susceptibility peaks at intermediate vegetation density S. Yuan et al. https://doi.org/10.1016/j.isci.2026.117271
525. Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression I. Colkesen et al. https://doi.org/10.1016/j.jafrearsci.2016.02.019
526. New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management C. de Oliveira Ferreira Silva et al. https://doi.org/10.1007/s42452-021-04600-w
527. Hybrid landslide susceptibility modelling for rainfall-triggered landslides in forested slopes: a review for bridging physics-based and machine learning methods M. Hashemi & R. Visser https://doi.org/10.33494/nzjfs562026x480x
528. Machine learning and GIS-RS-based algorithms for mapping the groundwater potentiality in the Bundelkhand region, India M. Kumar et al. https://doi.org/10.1016/j.ecoinf.2023.101980
529. Random Forest-Based Landslide Susceptibility Mapping in Coastal Regions of Artvin, Turkey H. Akinci et al. https://doi.org/10.3390/ijgi9090553
530. Application of Machine Learning on Google Earth Engine to Produce Landslide Susceptibility Mapping (Case Study: Pacitan) H. Ilmy et al. https://doi.org/10.1088/1755-1315/731/1/012028
531. Meta-learning an intermediate representation for few-shot prediction of landslide susceptibility in large areas L. Chen et al. https://doi.org/10.1016/j.jag.2022.102807
532. Enhanced dynamic landslide hazard mapping using MT-InSAR method in the Three Gorges Reservoir Area C. Zhou et al. https://doi.org/10.1007/s10346-021-01796-1
533. Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms W. Chen et al. https://doi.org/10.1007/s10064-017-1004-9
534. Development of integrated deep learning and machine learning algorithm for the assessment of landslide hazard potential B. Aslam et al. https://doi.org/10.1007/s00500-021-06105-5
535. Machine Learning-Based Evaluation of Susceptibility to Geological Hazards in the Hengduan Mountains Region, China J. Zhao et al. https://doi.org/10.1007/s13753-022-00401-w
536. Assessment of spatial distribution of rain-induced and earthquake-triggered landslides using geospatial techniques along North Sikkim Road Corridor in Sikkim Himalayas, India B. Koley et al. https://doi.org/10.1007/s10708-022-10585-9
537. Assessing the Generalization of Machine Learning-Based Slope Failure Prediction to New Geographic Extents A. Maxwell et al. https://doi.org/10.3390/ijgi10050293
538. Multi-temporal InSAR-based landslide dynamic susceptibility mapping of Fengjie County, Three Gorges Reservoir Area, China F. Tian et al. https://doi.org/10.1016/j.jrmge.2025.01.012
539. Integration of Information Theory, K-Means Cluster Analysis and the Logistic Regression Model for Landslide Susceptibility Mapping in the Three Gorges Area, China Q. Wang et al. https://doi.org/10.3390/rs9090938
540. A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs S. Lu et al. https://doi.org/10.3390/ma13173902
541. Evaluation and Prediction of Topsoil organic carbon using Machine learning and hybrid models at a Field-scale H. Matinfar et al. https://doi.org/10.1016/j.catena.2021.105258
542. Landslides in the Himalayas: The Role of Conditioning Factors and Their Resolution in Susceptibility Mapping L. Pathak et al. https://doi.org/10.3390/geosciences15040131
543. Susceptibility of existing and planned Chinese railway system subjected to rainfall-induced multi-hazards K. Liu et al. https://doi.org/10.1016/j.tra.2018.08.030
544. A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran E. Rafiei Sardooi et al. https://doi.org/10.1007/s12665-021-09788-z
545. Design and implementation of spatial database and geo-processing models for a road geo-hazard information management and risk assessment system W. Wang et al. https://doi.org/10.1007/s12665-014-3461-9
546. Geographically weighted random forests for macro-level crash frequency prediction D. Wu et al. https://doi.org/10.1016/j.aap.2023.107370
547. High-Resolution Downscaling of GRACE-Derived Groundwater Storage Anomalies using Stacking Ensemble Machine Learning in the Data-Scarce Tropical Catchments S. Karunarathna et al. https://doi.org/10.1007/s41748-026-01022-9
548. Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy H. Hong et al. https://doi.org/10.1080/10106049.2015.1130086
549. Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models H. Ahmad et al. https://doi.org/10.3390/ijgi10050315
550. Generating soil thickness maps by means of geomorphological-empirical approach and random forest algorithm in Wanzhou County, Three Gorges Reservoir T. Xiao et al. https://doi.org/10.1016/j.gsf.2022.101514
551. How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? — A catchment-scale case study from China Z. Guo et al. https://doi.org/10.1016/j.jrmge.2023.07.026
552. Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: a case study in northern Iran S. Razavizadeh et al. https://doi.org/10.1007/s12665-017-6839-7
553. Application of a two-step sampling strategy based on deep neural network for landslide susceptibility mapping J. Yao et al. https://doi.org/10.1007/s10064-022-02615-0
554. The SWADE model for landslide dating in time series of optical satellite imagery S. Fu et al. https://doi.org/10.1007/s10346-022-02012-4
555. Integrating Data Modality and Statistical Learning Methods for Earthquake-Induced Landslide Susceptibility Mapping Z. Miao et al. https://doi.org/10.3390/app12031760
556. Landslide susceptibility mapping using hybrid machine learning classifiers: a case study of Neelum Valley, Pakistan S. Meena et al. https://doi.org/10.1007/s10064-025-04270-7
557. Implementing an explored advanced and integrated deep random forest learning-based model to monitor the enhanced landslide susceptibility mapping Y. Mao et al. https://doi.org/10.1007/s11069-025-07415-9
558. Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions S. Abdollahi et al. https://doi.org/10.1007/s10064-018-1403-6
559. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy) A. Trigila et al. https://doi.org/10.1016/j.geomorph.2015.06.001
560. Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms M. Panahi et al. https://doi.org/10.1016/j.scitotenv.2020.139937
561. Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey H. Akinci & M. Zeybek https://doi.org/10.1007/s11069-021-04743-4
562. A novel model for regional susceptibility mapping of rainfall-reservoir induced landslides in Jurassic slide-prone strata of western Hubei Province, Three Gorges Reservoir area J. Long et al. https://doi.org/10.1007/s00477-020-01892-z
563. Rapid Mapping of Landslides on SAR Data by Attention U-Net L. Nava et al. https://doi.org/10.3390/rs14061449
564. Slope-Unit Scale Landslide Susceptibility Mapping Based on the Random Forest Model in Deep Valley Areas H. Deng et al. https://doi.org/10.3390/rs14174245
565. A methodological approach of QRA for slow-moving landslides at a regional scale F. Caleca et al. https://doi.org/10.1007/s10346-022-01875-x
566. Analysis of the Differences Between Two Landslides on One Slope in Yongguang Village Based on Physical Models and Groundwater Identification F. Lu et al. https://doi.org/10.3390/w16243591
567. Mapping Co-seismic Landslide Susceptibility: An Overview of Current and Emerging Methods K. Gupta et al. https://doi.org/10.1007/s40098-026-01495-5
568. Machine learning based landslide susceptibility mapping models and GB-SAR based landslide deformation monitoring systems: Growth and evolution B. Ganesh et al. https://doi.org/10.1016/j.rsase.2022.100905
569. 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. https://doi.org/10.3390/rs15051376
570. Co-Seismic Landslides Triggered by the 2014 Mw 6.2 Ludian Earthquake, Yunnan, China: Spatial Distribution, Directional Effect, and Controlling Factors Y. Duan et al. https://doi.org/10.3390/rs15184444