Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

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

(2022) Gizem Karakas et al.   Environmental Earth Sciences

Landslide susceptibility mapping: improvements in variable weights estimation through machine learning algorithms—a case study of upper Indus River Basin, Pakistan

(2022) Iqra Imtiaz et al.   Environmental Earth Sciences

Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping

(2022) Rui Liu et al.   Remote Sensing

Landslide Susceptibility Mapping with Deep Learning Algorithms

(2022) Jules Maurice Habumugisha et al.   Sustainability

Slope stability prediction using ensemble learning techniques: A case study in Yunyang County, Chongqing, China

(2022) Wengang Zhang et al.   Journal of Rock Mechanics and Geotechnical Engineering

An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost

(2022) Xinzhi Zhou et al.   Geocarto International

A Hybrid Optimization Method of Factor Screening Predicated on GeoDetector and Random Forest for Landslide Susceptibility Mapping

(2021) Deliang Sun et al.   GEOMORPHOLOGY

Landslide Susceptibility Mapping Using Rotation Forest Ensemble Technique with Different Decision Trees in the Three Gorges Reservoir Area, China

(2021) Zhice Fang et al.   Remote Sensing

Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping

(2021) Luísa Vieira Lucchese et al.   Natural Hazards

Decision Tree based ensemble machine learning approaches for landslide susceptibility mapping

(2021) Alireza Arabameri et al.   Geocarto International

A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China

(2021) Wei Xie et al.   ISPRS International Journal of Geo-Information

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

(2021) Xudong Hu et al.   Bulletin of Engineering Geology and the Environment

Machine learning in earthquake- and typhoon-triggered landslide susceptibility mapping and critical factor identification

(2021) Muhammad Zeeshan Ali et al.   Environmental Earth Sciences

Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey

(2021) Halil Akinci et al.   Natural Hazards

Landslide susceptibility mapping using state-of-the-art machine learning ensembles

(2021) Binh Thai Pham et al.   Geocarto International

Risk Factor Detection and Landslide Susceptibility Mapping Using Geo-Detector and Random Forest Models: The 2018 Hokkaido Eastern Iburi Earthquake

(2021) Yimo Liu et al.   Remote Sensing

A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping

(2021) Zhu Liang et al.   Remote Sensing

Slope Unit-Based Landslide Susceptibility Mapping Using Certainty Factor, Support Vector Machine, Random Forest, CF-SVM and CF-RF Models

(2021) Zhou Zhao et al.   Frontiers in Earth Science

Retrospective evaluation of landslide susceptibility maps and review of validation practice

(2021) Paul Fleuchaus et al.   Environmental Earth Sciences

GIS-based landslide susceptibility mapping using ensemble methods for Fengjie County in the Three Gorges Reservoir Region, China

(2021) W. Gong et al.   International Journal of Environmental Science and Technology

Landslide zonation and assessment of Farizi watershed in northeastern Iran using data mining techniques

(2021) Mahnaz Naemitabar et al.   NATURAL HAZARDS

Bagging-based machine learning algorithms for landslide susceptibility modeling

(2021) Tingyu Zhang et al.   NATURAL HAZARDS

Stacking ensemble of deep learning methods for landslide susceptibility mapping in the Three Gorges Reservoir area, China

(2021) Wenjuan Li et al.   STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT

Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization

(2021) Xinzhi Zhou et al.   Geoscience Frontiers

Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China

(2021) Yin Xing et al.   Frontiers in Earth Science

Hybrid ensemble machine learning approaches for landslide susceptibility mapping using different sampling ratios at East Sikkim Himalayan, India

(2021) Sunil Saha et al.   ADVANCES IN SPACE RESEARCH

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

(2021) Huijuan Zhang et al.   COMPUTERS & GEOSCIENCES

Deep learning-based landslide susceptibility mapping

(2021) Mohammad Azarafza et al.   Scientific Reports

Combining logistic regression-based hybrid optimized machine learning algorithms with sensitivity analysis to achieve robust landslide susceptibility mapping

(2021) Saeed Alqadhi et al.   Geocarto International

Comparison between Deep Learning and Tree-Based Machine Learning Approaches for Landslide Susceptibility Mapping

(2021) Sunil Saha et al.   Water

Exploring Complementary Models Consisting of Machine Learning Algorithms for Landslide Susceptibility Mapping

(2021) Han Hu et al.   ISPRS International Journal of Geo-Information

Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping

(2020) Guirong Wang et al.   Symmetry-Basel

GIS-Based Ensemble Soft Computing Models for Landslide Susceptibility Mapping

(2020) Binh Thai Pham et al.   ADVANCES IN SPACE RESEARCH

Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area

(2020) Viet-Ha Nhu et al.   CATENA

A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China

(2020) Yumiao Wang et al.   CATENA

Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability

(2020) Mariano Di Napoli et al.   Landslides

Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

(2020) Viet-Ha Nhu et al.   Forests

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

(2020) Viet-Ha Nhu et al.   International Journal of Environmental Research and Public Health

Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data

(2020) Bahareh Kalantar et al.   Remote Sensing

Evaluating the Performance of Individual and Novel Ensemble of Machine Learning and Statistical Models for Landslide Susceptibility Assessment at Rudraprayag District of Garhwal Himalaya

(2020) Sunil Saha et al.   Applied Sciences-Basel

Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping

(2020) Zhice Fang et al.   COMPUTERS & GEOSCIENCES

Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance

(2020) Abdelaziz Merghadi et al.   EARTH-SCIENCE REVIEWS

Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China)

(2020) Yue Wang et al.   International Journal of Environmental Research and Public Health

Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment

(2020) Viet-Ha Nhu et al.   International Journal of Environmental Research and Public Health

Landslide Susceptibility Mapping Using the Stacking Ensemble Machine Learning Method in Lushui, Southwest China

(2020) Xudong Hu et al.   Applied Sciences-Basel

Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia

(2020) Ahmed Mohamed Youssef et al.   Geoscience Frontiers

Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

(2020) Phuong Thao Thi Ngo et al.   Geoscience Frontiers

A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping

(2020) Zhice Fang et al.   INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE

Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China

(2020) Xudong Hu et al.   Natural Hazards

Application and comparison of different ensemble learning machines combining with a novel sampling strategy for shallow landslide susceptibility mapping

(2020) Zhu Liang et al.   STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT

Rainfall Induced Landslide Susceptibility Mapping Using Novel Hybrid Soft Computing Methods Based on Multi-layer Perceptron Neural Network Classifier

(2020) Mehebub Sahana et al.   Geocarto International

Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping

(2020) Mohammed Sarfaraz Gani Adnan et al.   Remote Sensing

Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping

(2020) Binh Thai Pham et al.   CATENA

Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments

(2020) Wei Chen et al.   Remote Sensing

Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China

(2019) Yumiao Wang et al.   International Journal of Environmental Research and Public Health

Landslide Susceptibility Mapping using Naïve Bayes and Bayesian Network Models in Umyeonsan, Korea

(2019) Sunmin Lee et al.   Geocarto International

Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree

(2019) Wei Chen et al.   Geocarto International

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

(2019) Alireza Arabameri et al.   Journal of Mountain Science

Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China

(2019) Yi Wang et al.   SCIENCE OF THE TOTAL ENVIRONMENT

GIS-based landslide susceptibility mapping using hybrid integration approaches of fractal dimension with index of entropy and support vector machine

(2019) Ting-yu Zhang et al.   Journal of Mountain Science

Performance Analysis of Advanced Decision Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping

(2019) Emrehan Kutlug Sahin et al.   Geocarto International

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

(2019) Renwei Li et al.   Symmetry-Basel

Landslide susceptibility mapping of the Mediterranean coastal zone of Morocco between Oued Laou and El Jebha using artificial neural networks (ANN)

(2019) Hasnaa Harmouzi et al.   Arabian Journal of Geosciences

A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping

(2019) Pham et al.   Sustainability

Machine learning and fractal theory models for landslide susceptibility mapping: Case study from the Jinsha River Basin

(2019) Qiao Hu et al.   GEOMORPHOLOGY

A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India

(2019) Jagabandhu Roy et al.   Remote Sensing

Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

(2019) Yang Li et al.   Water

Review on landslide susceptibility mapping using support vector machines

(2018) Yu Huang et al.   CATENA

A review of statistically-based landslide susceptibility models

(2018) Paola Reichenbach et al.   EARTH-SCIENCE REVIEWS

GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique

(2018) Alireza Arabameri et al.   Environmental Earth Sciences

A Comparative Assessment of Canonical Correlation Forest, Random Forest, Rotation Forest and Logistic Regression Methods for Landslide Susceptibility Mapping

(2018) Emrehan Kutlug Sahin et al.   Geocarto International

Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping

(2018) Prima Kadavi et al.   Remote Sensing

Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

(2018) Ataollah Shirzadi et al.   SENSORS

Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches

(2018) Binh Thai Pham et al.   CATENA

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

(2017) Zhengtuan Xie et al.   Environmental Earth Sciences

Evaluation and comparison of LogitBoost Ensemble, Fisher’s Linear Discriminant Analysis, logistic regression and support vector machines methods for landslide susceptibility mapping

(2017) Binh Thai Pham et al.   Geocarto International

Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea

(2017) Jeong-Cheol Kim et al.   Geocarto International

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)

(2017) Bahareh Kalantar et al.   Geomatics Natural Hazards & Risk

Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China

(2016) Wei Chen et al.   Environmental Earth Sciences

GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks

(2016) Dieu Tien Bui et al.   Environmental Earth Sciences

A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)

(2016) Binh Thai Pham et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Landslide susceptibility mapping at Gongliu county, China using artificial neural network and weight of evidence models

(2016) Qiqing Wang et al.   GEOSCIENCES JOURNAL

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

(2016) Xianyu Yu et al.   International Journal of Environmental Research and Public Health

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

(2015) Qingkai Meng et al.   Bulletin of Engineering Geology and the Environment

Landslide inventories for climate impacts research in the European Alps

(2015) J.L. Wood et al.   GEOMORPHOLOGY

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

(2015) Ahmed Mohamed Youssef et al.   Landslides

Deep learning

(2015) Yann LeCun et al.   NATURE

A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping

(2014) Omar F. Althuwaynee et al.   Landslides

Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement

(2013) David Moher   ANNALS OF INTERNAL MEDICINE

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

(2013) Mohammad Zare et al.   Arabian Journal of Geosciences

Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China

(2013) Ling Peng et al.   GEOMORPHOLOGY

Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran

(2013) HAMID REZA POURGHASEMI et al.   Journal of Earth System Science

Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping

(2013) Natan Micheletti et al.   Mathematical Geosciences

Landslide hazard assessment: recent trends and techniques

(2013) Sudhakar D Pardeshi et al.   SpringerPlus

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

(2012) Biswajeet Pradhan   COMPUTERS & GEOSCIENCES

Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea

(2012) Soyoung Park et al.   Environmental Earth Sciences

GIS-based landslide susceptibility mapping using analytic hierarchy process and artificial neural network in Jeju (Korea)

(2012) He-Chun Quan et al.   KSCE Journal of Civil Engineering

Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran

(2012) Hamid Reza Pourghasemi et al.   NATURAL HAZARDS

Landslide susceptibility assessment using SVM machine learning algorithm

(2011) Miloš Marjanović et al.   ENGINEERING GEOLOGY

A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks

(2009) Işık Yilmaz   Bulletin of Engineering Geology and the Environment

Validation of an artificial neural network model for landslide susceptibility mapping

(2009) Jaewon Choi et al.   Environmental Earth Sciences

The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks

(2009) Işık Yilmaz   Environmental Earth Sciences

Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine

(2009) Işık Yilmaz   Environmental Earth Sciences

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

(2009) H. P. Sato et al.   Landslides

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

(2008) Işık Yilmaz   COMPUTERS & GEOSCIENCES

Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China

(2008) X. Yao et al.   GEOMORPHOLOGY

A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability

(2008) S. García et al.   SOFT COMPUTING