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Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Applicational aspects of support vector machines
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Digital terrain modelling: A review of hydrological, geomorphological, and biological applications
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A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
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Interdisciplinary Computing in Java Programming
inferredcites →https://doi.org/10.1007/978-3-319-77377-3_13openalex-snapshot
Machine Learning Techniques in Landslide Susceptibility Mapping: A Survey and a Case Study
inferredcites →https://doi.org/10.1007/978-3-642-34062-8_32openalex-snapshot
New Machine Learning Algorithm: Random Forest
inferredcites →https://doi.org/10.1007/bf02590170openalex-snapshot
Slope instability phenomena in Greece: A statistical analysis
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Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach
inferredcites →https://doi.org/10.1007/s002540100310openalex-snapshot
Statistical analysis of landslide susceptibility at Yongin, Korea
inferredcites →https://doi.org/10.1007/s00477-020-01893-yopenalex-snapshot
Application and comparison of different ensemble learning machines combining with a novel sampling strategy for shallow landslide susceptibility mapping
inferredcites →https://doi.org/10.1007/s00477-021-02032-xopenalex-snapshot
Stacking ensemble of deep learning methods for landslide susceptibility mapping in the Three Gorges Reservoir area, China
inferredcites →https://doi.org/10.1007/s00500-008-0392-yopenalex-snapshot
A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability
inferredcites →https://doi.org/10.1007/s10064-009-0185-2openalex-snapshot
A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks
inferredcites →https://doi.org/10.1007/s10064-015-0786-xopenalex-snapshot
GIS-based landslide susceptibility mapping with logistic regression, analytical hierarchy process, and combined fuzzy and support vector machine methods: a case study from Wolong Giant Panda Natural Reserve, China
inferredcites →https://doi.org/10.1007/s10064-017-1034-3openalex-snapshot
Landslide susceptibility mapping at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms
inferredcites →https://doi.org/10.1007/s10064-021-02275-6openalex-snapshot
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
inferredcites →https://doi.org/10.1007/s10346-009-0147-6openalex-snapshot
Interpretation of earthquake-induced landslides triggered by the 12 May 2008, M7.9 Wenchuan earthquake in the Beichuan area, Sichuan Province, China using satellite imagery and Google Earth
inferredcites →https://doi.org/10.1007/s10346-014-0466-0openalex-snapshot
A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping
inferredcites →https://doi.org/10.1007/s10346-015-0614-1openalex-snapshot
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
inferredcites →https://doi.org/10.1007/s10346-020-01392-9openalex-snapshot
Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability
inferredcites →https://doi.org/10.1007/s11004-013-9511-0openalex-snapshot
Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping
inferredcites →https://doi.org/10.1007/s11069-012-0217-2openalex-snapshot
Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran
inferredcites →https://doi.org/10.1007/s11069-020-04371-4openalex-snapshot
Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China
inferredcites →https://doi.org/10.1007/s11069-021-04547-6openalex-snapshot
Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping
inferredcites →https://doi.org/10.1007/s11069-021-04743-4openalex-snapshot
Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey
inferredcites →https://doi.org/10.1007/s11069-021-04805-7openalex-snapshot
Landslide zonation and assessment of Farizi watershed in northeastern Iran using data mining techniques
inferredcites →https://doi.org/10.1007/s11069-021-04986-1openalex-snapshot
Bagging-based machine learning algorithms for landslide susceptibility modeling
inferredcites →https://doi.org/10.1007/s11629-018-5168-yopenalex-snapshot
GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms
inferredcites →https://doi.org/10.1007/s11629-018-5337-zopenalex-snapshot
GIS-based landslide susceptibility mapping using hybrid integration approaches of fractal dimension with index of entropy and support vector machine
inferredcites →https://doi.org/10.1007/s12040-013-0282-2openalex-snapshot
Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran
inferredcites →https://doi.org/10.1007/s12205-012-1242-0openalex-snapshot
GIS-based landslide susceptibility mapping using analytic hierarchy process and artificial neural network in Jeju (Korea)
inferredcites →https://doi.org/10.1007/s12303-016-0003-3openalex-snapshot
Landslide susceptibility mapping at Gongliu county, China using artificial neural network and weight of evidence models
inferredcites →https://doi.org/10.1007/s12517-012-0610-xopenalex-snapshot
Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms
inferredcites →https://doi.org/10.1007/s12517-019-4892-0openalex-snapshot
Landslide susceptibility mapping of the Mediterranean coastal zone of Morocco between Oued Laou and El Jebha using artificial neural networks (ANN)
inferredcites →https://doi.org/10.1007/s12665-009-0188-0openalex-snapshot
Validation of an artificial neural network model for landslide susceptibility mapping
inferredcites →https://doi.org/10.1007/s12665-009-0191-5openalex-snapshot
The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks
inferredcites →https://doi.org/10.1007/s12665-009-0394-9openalex-snapshot
Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine
inferredcites →https://doi.org/10.1007/s12665-012-1842-5openalex-snapshot
Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea
inferredcites →https://doi.org/10.1007/s12665-015-5093-0openalex-snapshot
Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China
inferredcites →https://doi.org/10.1007/s12665-016-5919-4openalex-snapshot
GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks
inferredcites →https://doi.org/10.1007/s12665-017-6640-7openalex-snapshot
A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin, China
inferredcites →https://doi.org/10.1007/s12665-018-7808-5openalex-snapshot
GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique
inferredcites →https://doi.org/10.1007/s12665-021-09510-zopenalex-snapshot
Machine learning in earthquake- and typhoon-triggered landslide susceptibility mapping and critical factor identification
inferredcites →https://doi.org/10.1007/s12665-021-09770-9openalex-snapshot
Retrospective evaluation of landslide susceptibility maps and review of validation practice
inferredcites →https://doi.org/10.1007/s12665-022-10225-yopenalex-snapshot
Comprehensive performance assessment of landslide susceptibility mapping with MLP and random forest: a case study after Elazig earthquake (24 Jan 2020, Mw 6.8), Turkey
inferredcites →https://doi.org/10.1007/s12665-022-10233-yopenalex-snapshot
Landslide susceptibility mapping: improvements in variable weights estimation through machine learning algorithms—a case study of upper Indus River Basin, Pakistan
inferredcites →https://doi.org/10.1007/s13369-022-06560-8openalex-snapshot
Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost)
inferredcites →https://doi.org/10.1007/s13762-021-03572-zopenalex-snapshot
GIS-based landslide susceptibility mapping using ensemble methods for Fengjie County in the Three Gorges Reservoir Region, China
inferredcites →https://doi.org/10.1016/b978-0-12-821379-7.00003-5openalex-snapshot
Machine learning techniques
inferredcites →https://doi.org/10.1016/b978-0-12-821929-4.00004-4openalex-snapshot
Supervised learning
inferredcites →https://doi.org/10.1016/j.asr.2020.05.016openalex-snapshot
GIS-based ensemble soft computing models for landslide susceptibility mapping
inferredcites →https://doi.org/10.1016/j.asr.2021.05.018openalex-snapshot
Hybrid ensemble machine learning approaches for landslide susceptibility mapping using different sampling ratios at East Sikkim Himalayan, India
inferredcites →https://doi.org/10.1016/j.cageo.2008.08.007openalex-snapshot
Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)
inferredcites →https://doi.org/10.1016/j.cageo.2012.08.023openalex-snapshot
A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS
inferredcites →https://doi.org/10.1016/j.cageo.2020.104470openalex-snapshot
Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping
inferredcites →https://doi.org/10.1016/j.cageo.2021.104966openalex-snapshot
Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China
inferredcites →https://doi.org/10.1016/j.catena.2016.09.007openalex-snapshot
Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS
inferredcites →https://doi.org/10.1016/j.catena.2018.03.003openalex-snapshot
Review on landslide susceptibility mapping using support vector machines
inferredcites →https://doi.org/10.1016/j.catena.2018.12.018openalex-snapshot
Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches
inferredcites →https://doi.org/10.1016/j.catena.2019.104425openalex-snapshot
A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China
inferredcites →https://doi.org/10.1016/j.catena.2020.104458openalex-snapshot
Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area
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Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping
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A review of statistically-based landslide susceptibility models
inferredcites →https://doi.org/10.1016/j.earscirev.2020.103225openalex-snapshot
Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance
inferredcites →https://doi.org/10.1016/j.enggeo.2004.06.001openalex-snapshot
Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey)
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Landslide susceptibility assessment using SVM machine learning algorithm
inferredcites →https://doi.org/10.1016/j.envsoft.2016.07.005openalex-snapshot
A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)
inferredcites →https://doi.org/10.1016/j.geomorph.2008.02.011openalex-snapshot
Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China
inferredcites →https://doi.org/10.1016/j.geomorph.2013.08.013openalex-snapshot
Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China
inferredcites →https://doi.org/10.1016/j.geomorph.2014.09.005openalex-snapshot
Landslide inventories for climate impacts research in the European Alps
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Machine learning and fractal theory models for landslide susceptibility mapping: Case study from the Jinsha River Basin
inferredcites →https://doi.org/10.1016/j.geomorph.2021.107623openalex-snapshot
A hybrid optimization method of factor screening predicated on GeoDetector and Random Forest for Landslide Susceptibility Mapping
inferredcites →https://doi.org/10.1016/j.gsf.2020.05.010openalex-snapshot
Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia
inferredcites →https://doi.org/10.1016/j.gsf.2020.06.013openalex-snapshot
Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran
inferredcites →https://doi.org/10.1016/j.gsf.2021.101211openalex-snapshot
Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization
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Creating diversity in ensembles using artificial data
inferredcites →https://doi.org/10.1016/j.jag.2022.102681openalex-snapshot
Combining spatial response features and machine learning classifiers for landslide susceptibility mapping
inferredcites →https://doi.org/10.1016/j.jrmge.2021.12.011openalex-snapshot
Slope stability prediction using ensemble learning techniques: A case study in Yunyang County, Chongqing, China
inferredcites →https://doi.org/10.1016/j.scitotenv.2019.02.263openalex-snapshot
Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China
inferredcites →https://doi.org/10.1016/s0013-7952(97)81260-4openalex-snapshot
Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques
inferredcites →https://doi.org/10.1016/s0169-555x(02)00355-0openalex-snapshot
Landslide inventory in a rugged forested watershed: a comparison between air-photo and field survey data
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Landslides and climate change in the Italian Dolomites since the Late glacial
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Logistic regression and artificial neural network classification models: a methodology review
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Improved Boosting Algorithms Using Confidence-rated Predictions
inferredcites →https://doi.org/10.1023/a:1007659514849openalex-snapshot
MultiBoosting: A Technique for Combining Boosting and Wagging
inferredcites →https://doi.org/10.1038/nbt1206-1565openalex-snapshot
What is a support vector machine?
inferredcites →https://doi.org/10.1038/s41598-021-03585-1openalex-snapshot
Deep learning-based landslide susceptibility mapping
inferredcites →https://doi.org/10.1080/02626667909491834openalex-snapshot
A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant
inferredcites →https://doi.org/10.1080/10106049.2017.1323964openalex-snapshot
Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea
inferredcites →https://doi.org/10.1080/10106049.2017.1404141openalex-snapshot
Evaluation and comparison of LogitBoost Ensemble, Fisher’s Linear Discriminant Analysis, logistic regression and support vector machines methods for landslide susceptibility mapping
inferredcites →https://doi.org/10.1080/10106049.2018.1516248openalex-snapshot
A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping
inferredcites →https://doi.org/10.1080/10106049.2019.1585482openalex-snapshot
Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea
inferredcites →https://doi.org/10.1080/10106049.2019.1588393openalex-snapshot
Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree
inferredcites →https://doi.org/10.1080/10106049.2019.1641560openalex-snapshot
Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping
inferredcites →https://doi.org/10.1080/10106049.2020.1837262openalex-snapshot
Rainfall induced landslide susceptibility mapping using novel hybrid soft computing methods based on multi-layer perceptron neural network classifier
inferredcites →https://doi.org/10.1080/10106049.2021.1892210openalex-snapshot
Decision tree based ensemble machine learning approaches for landslide susceptibility mapping
inferredcites →https://doi.org/10.1080/10106049.2021.1914746openalex-snapshot
Landslide susceptibility mapping using state-of-the-art machine learning ensembles
inferredcites →https://doi.org/10.1080/10106049.2021.2022009openalex-snapshot
Combining logistic regression-based hybrid optimized machine learning algorithms with sensitivity analysis to achieve robust landslide susceptibility mapping
inferredcites →https://doi.org/10.1080/10106049.2022.2076928openalex-snapshot
An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost
inferredcites →https://doi.org/10.1080/13658816.2020.1808897openalex-snapshot
A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping
inferredcites →https://doi.org/10.1080/19475705.2017.1407368openalex-snapshot
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)
inferredcites →https://doi.org/10.1080/19475705.2018.1481147openalex-snapshot
Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models
inferredcites →https://doi.org/10.1080/19475705.2021.1943544openalex-snapshot
Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin
inferredcites →https://doi.org/10.1080/19475705.2021.1944330openalex-snapshot
A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping
inferredcites →https://doi.org/10.1080/20964471.2018.1472392openalex-snapshot
Mapping landslide susceptibility and types using Random Forest
inferredcites →https://doi.org/10.1109/34.709601openalex-snapshot
The random subspace method for constructing decision forests
inferredcites →https://doi.org/10.1109/incos.2009.25openalex-snapshot
Landslide Susceptibility Assessment with Machine Learning Algorithms
inferredcites →https://doi.org/10.1109/jstars.2020.3014143openalex-snapshot
Potential of Ensemble Learning to Improve Tree-Based Classifiers for Landslide Susceptibility Mapping
inferredcites →https://doi.org/10.1109/tpami.2006.211openalex-snapshot
Rotation Forest: A New Classifier Ensemble Method
inferredcites →https://doi.org/10.1130/2012.2492(01)openalex-snapshot
Channel widths, landslides, faults, and beyond: The new world order of high-spatial resolution Google Earth imagery in the study of earth surface processes
inferredcites →https://doi.org/10.1177/001316446002000104openalex-snapshot
A Coefficient of Agreement for Nominal Scales
inferredcites →https://doi.org/10.1186/2193-1801-2-523openalex-snapshot
Landslide hazard assessment: recent trends and techniques
inferredcites →https://doi.org/10.1186/s13750-018-0121-7openalex-snapshot
ROSES RepOrting standards for Systematic Evidence Syntheses: pro forma, flow-diagram and descriptive summary of the plan and conduct of environmental systematic reviews and systematic maps
inferredcites →https://doi.org/10.1191/0309133305pp462raopenalex-snapshot
GIS-based landslide hazard assessment: an overview
inferredcites →https://doi.org/10.12989/gae.2021.25.1.001openalex-snapshot
Landslide susceptibility assessment using feature selection-based machine learning models
inferredcites →https://doi.org/10.3389/feart.2021.589630openalex-snapshot
Slope Unit-Based Landslide Susceptibility Mapping Using Certainty Factor, Support Vector Machine, Random Forest, CF-SVM and CF-RF Models
inferredcites →https://doi.org/10.3389/feart.2021.722491openalex-snapshot
Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China
inferredcites →https://doi.org/10.3390/app10113772openalex-snapshot
Evaluating the Performance of Individual and Novel Ensemble of Machine Learning and Statistical Models for Landslide Susceptibility Assessment at Rudraprayag District of Garhwal Himalaya
inferredcites →https://doi.org/10.3390/app10114016openalex-snapshot
Landslide Susceptibility Mapping Using the Stacking Ensemble Machine Learning Method in Lushui, Southwest China
inferredcites →https://doi.org/10.3390/f11040421openalex-snapshot
Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran
inferredcites →https://doi.org/10.3390/ijerph13050487openalex-snapshot
A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China
inferredcites →https://doi.org/10.3390/ijerph16030368openalex-snapshot
Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China
inferredcites →https://doi.org/10.3390/ijerph17082749openalex-snapshot
Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms
inferredcites →https://doi.org/10.3390/ijerph17124206openalex-snapshot
Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China)
inferredcites →https://doi.org/10.3390/ijerph17144933openalex-snapshot
Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment
inferredcites →https://doi.org/10.3390/ijgi10020093openalex-snapshot
A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China
inferredcites →https://doi.org/10.3390/ijgi10100639openalex-snapshot
Exploring Complementary Models Consisting of Machine Learning Algorithms for Landslide Susceptibility Mapping
inferredcites →https://doi.org/10.3390/land10090989openalex-snapshot
Factors Affecting Landslide Susceptibility Mapping: Assessing the Influence of Different Machine Learning Approaches, Sampling Strategies and Data Splitting
inferredcites →https://doi.org/10.3390/rs10081252openalex-snapshot
Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping
inferredcites →https://doi.org/10.3390/rs11232866openalex-snapshot
A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India
inferredcites →https://doi.org/10.3390/rs12111737openalex-snapshot
Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data
inferredcites →https://doi.org/10.3390/rs12203347openalex-snapshot
Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping
inferredcites →https://doi.org/10.3390/rs12213518openalex-snapshot
Digital Image Correlation of Google Earth Images for Earth’s Surface Displacement Estimation
inferredcites →https://doi.org/10.3390/rs12233854openalex-snapshot
Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments
inferredcites →https://doi.org/10.3390/rs13020238openalex-snapshot
Landslide Susceptibility Mapping Using Rotation Forest Ensemble Technique with Different Decision Trees in the Three Gorges Reservoir Area, China
inferredcites →https://doi.org/10.3390/rs13061157openalex-snapshot
Risk Factor Detection and Landslide Susceptibility Mapping Using Geo-Detector and Random Forest Models: The 2018 Hokkaido Eastern Iburi Earthquake
inferredcites →https://doi.org/10.3390/rs13081464openalex-snapshot
A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping
inferredcites →https://doi.org/10.3390/rs13234776openalex-snapshot
Shared Blocks-Based Ensemble Deep Learning for Shallow Landslide Susceptibility Mapping
inferredcites →https://doi.org/10.3390/rs14020321openalex-snapshot
Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping
inferredcites →https://doi.org/10.3390/s18113777openalex-snapshot
Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping
inferredcites →https://doi.org/10.3390/su11226323openalex-snapshot
A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping
inferredcites →https://doi.org/10.3390/su14031734openalex-snapshot
Landslide Susceptibility Mapping with Deep Learning Algorithms
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Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression
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Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping
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Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques
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Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models—A Case Study of Shuicheng County, China
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Comparison between Deep Learning and Tree-Based Machine Learning Approaches for Landslide Susceptibility Mapping
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NGBoost: Natural Gradient Boosting for Probabilistic Prediction
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Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Insights into landslide susceptibility in different karst erosion landforms based on interpretable machine learning
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Comparison of different open‐source Digital Elevation Models for landslide susceptibility mapping
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A comprehensive review of machine learning‐based methods in landslide susceptibility mapping
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Advanced Machine Learning Models for Landslide Susceptibility Mapping in South Sikkim Himalayas, India
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Interpretable Landslide Hazard Analysis in the Western Ghats of Maharashtra, India: A Hybrid Machine Learning and Statistical Approach
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Assessment of Slope Instability in a Hilly Terrain: A Logistic Regression and Random Forest Based Approach
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The Accuracy of Landslide Susceptibility Mapping in Young Glacial River Valleys
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FastLSM-AutoML: Fast, reliable, and robust end-to-end AutoML tool for producing a landslide susceptibility map
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Uncertainty analysis method of slope safety factor based on quantile-based ensemble learning
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Optimizing reservoir landslide susceptibility mapping with physics-enhanced data-driven models
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Validation and prediction challenges in data-driven landslide susceptibility mapping: insights from random sampling of training and test data in machine learning models
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Integration of interpretable machine learning and PLUS model for dynamic landslide susceptibility mapping of urban settlements in the Three Gorges Reservoir area
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Machine learning-based landslide risk assessment in Doti district, western Nepal
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Handling data imbalance in machine learning based landslide susceptibility mapping: a case study of Mandakini River Basin, North-Western Himalayas
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Investigating landslide data balancing for susceptibility mapping using generative and machine learning models
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Improved landslide prediction by considering continuous and discrete spatial dependency
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The influence of sampling strategies on shallow landslide susceptibility modelling in a mountainous area
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Geoinformatics-based frequency ratio, analytic hierarchy process and hybrid models for landslide susceptibility zonation in Kurdistan Region, Northern Iraq
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Rainfall-Induced Slope Stability Modeling: A Systematic Review of Coupled and Hybrid Frameworks
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Comprehensive review of remote sensing integration with deep learning in landslide forecasting and future directions
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Enhancing flood mapping through ensemble machine learning in the Gamasyab watershed, Western Iran
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Predictive landslide susceptibility modeling in the southeastern hilly region of Bangladesh: application of machine learning algorithms in Khagrachari district
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Effectiveness of hybrid ensemble machine learning models for landslide susceptibility analysis: Evidence from Shimla district of North-west Indian Himalayan region
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Exploring class imbalance with under-sampling, over-sampling, and hybrid sampling based on Mahalanobis distance for landslide susceptibility assessment: a case study of the 2018 Iburi earthquake induced landslides in Hokkaido, Japan
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Machine Learning-Based Spatiotemporal Analysis for Landslide Susceptibility Mapping in the Hindu Kush Himalayan Region
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Machine Learning‐based landslide susceptibility analysis in volcanic islands: A comparison with traditional approaches
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Rule-based fuzzy inference system for landslide susceptibility mapping along national highway 7 in Garhwal Himalayas, India
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Slope-unit based landslide susceptibility mapping using explainable ensemble machine learning and spatial uncertainty analysis in the Chittagong Hill Tracts, Bangladesh
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Role of non-landslide data samples in improving landslide susceptibility mapping in the Western Ghats
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XAI-driven BERT variants for transparent Moroccan Darija toxic comments detection in social media
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Leakage-aware comparative machine learning for earthquake susceptibility mapping in the Rif region, Northern Morocco
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A new framework for landslide susceptibility mapping in contiguous impoverished areas using machine learning and catastrophe theory
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Landslide vulnerability assessment in East Java, Indonesia, using fuzzy analytical hierarchy process - Natural breaks
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Landslide susceptibility mapping using Frequency Ratio (FR) and Random Forest (RF) model in the Bandarban Hill District, Bangladesh
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Unraveling the evolution of landslide susceptibility: a systematic review of 30-years of strategic themes and trends
inferred← citeshttps://doi.org/10.1080/13658816.2025.2611986openalex-snapshot
A scalable graph transformer for large-scale landslide susceptibility mapping: a case study in the Upper Yellow River Basin
inferred← citeshttps://doi.org/10.1080/17499518.2024.2422498openalex-snapshot
Incorporating modelling uncertainty and prior knowledge into landslide susceptibility mapping using Bayesian neural networks
inferred← citeshttps://doi.org/10.1080/17538947.2024.2346263openalex-snapshot
Landslide susceptibility mapping using ensemble machine learning methods: a case study in Lombardy, Northern Italy
inferred← citeshttps://doi.org/10.1080/19475705.2023.2212833openalex-snapshot
Performance comparison of landslide susceptibility mapping under multiple machine-learning based models considering InSAR deformation: a case study of the upper Jinsha River
inferred← citeshttps://doi.org/10.1080/19475705.2024.2354507openalex-snapshot
A side-sampling based Linformer model for landslide susceptibility assessment: a case study of the railways in China
inferred← citeshttps://doi.org/10.1080/19475705.2026.2620641openalex-snapshot
Sampling effects on machine-learning performance and tectonic controls on landslide susceptibility: insights from the Adra River basin (SE Spain)
inferred← citeshttps://doi.org/10.1080/19475705.2026.2717867openalex-snapshot
Landslide susceptibility study using Bayesian networks combined with landslide physical mechanisms: a case study in Hebei Province, China
inferred← citeshttps://doi.org/10.1080/19475705.2026.2721807openalex-snapshot
A novel approach incorporating credibility and spatial uniformity for landslide negative sampling
inferred← citeshttps://doi.org/10.1080/19475705.2026.2729595openalex-snapshot
A GeoDetector–SHAP coupled explainable machine learning framework for landslide susceptibility mapping
inferred← citeshttps://doi.org/10.1080/19475705.2026.2731702openalex-snapshot
Landslide susceptibility assessment along China’s railways assisted by machine learning
inferred← citeshttps://doi.org/10.1088/1755-1315/1151/1/012037openalex-snapshot
Investigating causative factors and selecting optimal machine learning algorithms for landslide susceptibility assessment in Lom Kao area, northern Thailand
inferred← citeshttps://doi.org/10.1088/1757-899x/1289/1/012020openalex-snapshot
Landslide susceptibility modeling and mapping at Dien Bien province, Vietnam using Bagging based MLP neural network
inferred← citeshttps://doi.org/10.1088/2515-7620/ae3e50openalex-snapshot
Factors affecting landslides and IoT-based slope monitoring system in Southeast Asia: a review
inferred← citeshttps://doi.org/10.1109/access.2023.3291411openalex-snapshot
A New Approach Based on Balancing Composite Motion Optimization and Deep Neural Networks for Spatial Prediction of Landslides at Tropical Cyclone Areas
inferred← citeshttps://doi.org/10.1109/access.2024.3446037openalex-snapshot
Artificial Intelligence Techniques for Landslides Prediction Using Satellite Imagery
inferred← citeshttps://doi.org/10.1109/access.2025.3576854openalex-snapshot
Effects of SBAS-InSAR Deformation Integration Methods and Machine Learning Model Selection on Landslide Susceptibility Mapping
inferred← citeshttps://doi.org/10.1109/i3ctcon68242.2026.11507501openalex-snapshot
Smart Multi-Data Landslide Prediction System
inferred← citeshttps://doi.org/10.1109/icares64249.2024.10768099openalex-snapshot
Mo-BAPER: A Modified TabNet Employing Global Average Pooling for Bogor Area Landslide Prediction
inferred← citeshttps://doi.org/10.1109/icdt63985.2025.10986375openalex-snapshot
Enhanced Landslide Detection Using Deep Learning with Recurrent Neural Networks for Temporal Monitoring
inferred← citeshttps://doi.org/10.1109/icet67257.2025.11291009openalex-snapshot
Geographic Image Risk Prediction Using GIS and Machine Learning
inferred← citeshttps://doi.org/10.1109/icicml67980.2025.11333742openalex-snapshot
Landslide Susceptibility Mapping Using Optimized CNNs with Dynamic Learning Rate Strategy
inferred← citeshttps://doi.org/10.1109/icoici65217.2025.11253052openalex-snapshot
Enhancing Landslide Detection Accuracy and Efficiency Through Multi Scale Augmentation and Parallel Optimization Techniques
inferred← citeshttps://doi.org/10.1109/igarss53475.2024.10641963openalex-snapshot
Algorithmic Insights into Mapping Landslide Susceptibility: A Random Forest Approach in High-Risk Zones of Uttarakhand
inferred← citeshttps://doi.org/10.1111/tgis.70099openalex-snapshot
Artificial Intelligence in Landslide Susceptibility: A Bibliometric Analysis
inferred← citeshttps://doi.org/10.1111/tgis.70105openalex-snapshot
Inventory of Large‐Scale Landslide Traces in the Transition Zone Between the Qinghai‐Tibet Plateau and the Sichuan Basin
inferred← citeshttps://doi.org/10.1111/tgis.70317openalex-snapshot
Land Subsidence Susceptibility Mapping in Semi‐Arid Regions Using PS ‐ InSAR and Tree‐Based Ensemble Machine Learning: A Case Study From Çumra, Türkiye
inferred← citeshttps://doi.org/10.1117/12.3031770openalex-snapshot
An efficient technique for hazard susceptibility mapping using AHP and ML based models
inferred← citeshttps://doi.org/10.1155/2024/6616269openalex-snapshot
Landslide Susceptibility Mapping Using GIS and Bivariate Statistical Models in Chemoga Watershed, Ethiopia
inferred← citeshttps://doi.org/10.11648/j.ajasr.20261203.13openalex-snapshot
Comparative GIS-Based Landslide Susceptibility and Relative Risk Assessment Using Frequency Ratio, Shannon Entropy and Statistical Information Index Models
inferred← citeshttps://doi.org/10.1186/s40677-024-00271-yopenalex-snapshot
Landslide susceptibility assessment of South Korea using stacking ensemble machine learning
inferred← citeshttps://doi.org/10.1186/s40677-026-00382-8openalex-snapshot
Integration of machine learning model and CMIP6 analysis for climate change impact-led landslide susceptibility and population exposure assessments in the Nepal Himalaya
inferred← citeshttps://doi.org/10.12912/27197050/172569openalex-snapshot
Landslide Hazard Assessment in the Heterogeneous Geomorphological and Environmental Context of the Rif Region, Morocco – A Machine Learning Approach
inferred← citeshttps://doi.org/10.12944/cwe.19.2.29openalex-snapshot
Assessment of landslide susceptibility in the Himalayan state of Tripura, India, using a Multi-Model Approach
inferred← citeshttps://doi.org/10.13168/agg.2026.0008openalex-snapshot
Landslide susceptibility mapping of the Ha Long_Van Don Highway using novel ensemble models based on dual perturb and combine for three-based (DPCT)
inferred← citeshttps://doi.org/10.1371/journal.pone.0292897openalex-snapshot
Study on landslide susceptibility mapping with different factor screening methods and random forest models
inferred← citeshttps://doi.org/10.1515/geo-2022-0642openalex-snapshot
Comparative models of support-vector machine, multilayer perceptron, and decision tree predication approaches for landslide susceptibility analysis
inferred← citeshttps://doi.org/10.15531/ksccr.2026.17.3.517openalex-snapshot
Development of an AI-based landslide prediction model and nationwide early warning framework
inferred← citeshttps://doi.org/10.15625/2615-9783/18500openalex-snapshot
A novel HHO-RSCDT ensemble learning approach for forest fire danger mapping using GIS
inferred← citeshttps://doi.org/10.17491/jgsi/2024/174002openalex-snapshot
Spatial Prediction of Landslide Susceptibility using Various Machine Learning Based Binary Classification Methods
inferred← citeshttps://doi.org/10.17491/jgsi/2025/174103openalex-snapshot
Machine Learning-assisted Investigation of Landslide Susceptibility for Aglar Watershed in the Lesser Himalaya Region
inferred← citeshttps://doi.org/10.21203/rs.3.rs-10347129/v1openalex-snapshot
Machine-Learning-Based Landslide Susceptibility Mapping Using an InSAR- Refined Inventory in the Mandi district, Northwestern Himalaya, India, Himalaya region
inferred← citeshttps://doi.org/10.21203/rs.3.rs-11040778/v1openalex-snapshot
From Heuristic Models to GeoAI: A Scientometric and Thematic Evolution of Landslide Susceptibility Mapping Approaches
inferred← citeshttps://doi.org/10.21203/rs.3.rs-7694229/v1openalex-snapshot
A Comparative Study of TabNet and Classical Machine Learning Models for Landslide Prediction
inferred← citeshttps://doi.org/10.21203/rs.3.rs-8108895/v1openalex-snapshot
Mapping rain-induced landslides in data-scarce conflict-affected regions using deep learning model: A study from the Jebel Marra Volcanic Massif, Sudan
inferred← citeshttps://doi.org/10.21203/rs.3.rs-8913300/v1openalex-snapshot
Machine learning and deep learning for ground data deformation analytics: a comprehensive critical review
inferred← citeshttps://doi.org/10.21203/rs.3.rs-8913300/v2openalex-snapshot
Machine learning and deep learning for ground data deformation analytics: a comprehensive critical review
inferred← citeshttps://doi.org/10.21203/rs.3.rs-9218127/v1openalex-snapshot
Landslide Susceptibility Mapping around Alkumru Dam (Siirt, Türkiye) Using Machine Learning and Ensemble Models
inferred← citeshttps://doi.org/10.21285/2686-9993-2026-49-1-8openalex-snapshot
Comparative assessment of satellite and unmanned aerial vehicles data for landslide susceptibility mapping
inferred← citeshttps://doi.org/10.21324/dacd.1663672openalex-snapshot
Frekans Oranı ve Yapay Sinir Ağları Yöntemleri İle Samanlı Dağları Kütlesinde Heyelan Duyarlılık Analizi
inferred← citeshttps://doi.org/10.21595/vp.2025.25563openalex-snapshot
Urban terrain morphometry for transport planning: A GIS-based analysis of Tashkent using open DEMs
inferred← citeshttps://doi.org/10.2166/wpt.2024.002openalex-snapshot
Assessment of resampling methods on performance of landslide susceptibility predictions using machine learning in Kendari City, Indonesia
inferred← citeshttps://doi.org/10.2208/journalofjsce.23-27036openalex-snapshot
NUMERICAL MODELING OF LANDSLIDE SUSCEPTIBILITY: A CASE STUDY FOR UMA OYA CATCHMENT, SRI LANKA
inferred← citeshttps://doi.org/10.28991/cej-2023-09-12-018openalex-snapshot
Landslide Susceptibility Assessment in Western External Rif Chain using Machine Learning Methods
inferred← citeshttps://doi.org/10.3389/feart.2023.1268501openalex-snapshot
Machine learning for high-resolution landslide susceptibility mapping: case study in Inje County, South Korea
inferred← citeshttps://doi.org/10.3389/feart.2024.1419421openalex-snapshot
A comparative study of regional rainfall-induced landslide early warning models based on RF、CNN and MLP algorithms
inferred← citeshttps://doi.org/10.3389/feart.2026.1774847openalex-snapshot
Experimental study on the macroscopic and microscopic properties of the sliding zone soil in the Huaipa landslide under wetting-drying cycles
inferred← citeshttps://doi.org/10.3389/feart.2026.1876760openalex-snapshot
Influence of river incision on landslides triggered in Nepal by the 2015 Gorkha earthquake: results from a pixel-based susceptibility model using inlabru
inferred← citeshttps://doi.org/10.3389/fenvs.2024.1424988openalex-snapshot
Quantitative evaluation of uncertainty and interpretability in machine learning-based landslide susceptibility mapping through feature selection and explainable AI
inferred← citeshttps://doi.org/10.3390/app14125151openalex-snapshot
A Spatial Study on the Impact of Habitat Quality on Geological Disaster Susceptibility: A Case Study in Pingshan County, China
inferred← citeshttps://doi.org/10.3390/app14219639openalex-snapshot
Advancements in Technologies and Methodologies of Machine Learning in Landslide Susceptibility Research: Current Trends and Future Directions
inferred← citeshttps://doi.org/10.3390/app15031163openalex-snapshot
Insights from Optimized Non-Landslide Sampling and SHAP Explainability for Landslide Susceptibility Prediction
inferred← citeshttps://doi.org/10.3390/app15042038openalex-snapshot
Global Dynamic Landslide Susceptibility Modeling Based on ResNet18: Revealing Large-Scale Landslide Hazard Evolution Trends in China
inferred← citeshttps://doi.org/10.3390/app15116241openalex-snapshot
Rain-Induced Shallow Landslide Susceptibility Under Multiple Scenarios Based on Effective Antecedent Precipitation
inferred← citeshttps://doi.org/10.3390/app15158477openalex-snapshot
Machine Learning Ensemble Methods for Co-Seismic Landslide Susceptibility: Insights from the 2015 Nepal Earthquake
inferred← citeshttps://doi.org/10.3390/app15168955openalex-snapshot
The Impact of Feature Selection on XGBoost Performance in Landslide Susceptibility Mapping Using an Extended Set of Features: A Case Study from Southern Poland
inferred← citeshttps://doi.org/10.3390/app152011242openalex-snapshot
Ecoregion-Based Landslide Susceptibility Mapping: A Spatially Partitioned Modeling Strategy for Oregon, USA
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