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Scale Coupling Enables More Detailed Mapping of Landslide Susceptibility

https://doi.org/10.2139/ssrn.4685102
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Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression
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Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree
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Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan
inferredcites →https://doi.org/10.1007/s11069-012-0217-2openalex-extracted
Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran
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Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods
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Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil
inferredcites →https://doi.org/10.1007/s11069-021-04718-5openalex-extracted
GIS-based landslide susceptibility mapping using hybrid MCDM models
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Landslide susceptibility mapping of Darjeeling Himalaya, India using index of entropy (IOE) model
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Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches
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Robust landslide susceptibility analysis by combination of frequency ratio, heuristic GIS-methods and ground truth evaluation for a mountainous study area with poor data availability in the Three Gorges Reservoir area, PR China
inferredcites →https://doi.org/10.1007/s13762-021-03572-zopenalex-extracted
GIS-based landslide susceptibility mapping using ensemble methods for Fengjie County in the Three Gorges Reservoir Region, China
inferredcites →https://doi.org/10.1007/s41324-017-0160-0openalex-extracted
Comparative evaluation of information value and frequency ratio in landslide susceptibility analysis along national highways of Sikkim Himalaya
inferredcites →https://doi.org/10.1016/j.cageo.2020.104445openalex-extracted
Comparative study of landslide susceptibility mapping with different recurrent neural networks
inferredcites →https://doi.org/10.1016/j.cageo.2020.104470openalex-extracted
Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping
inferredcites →https://doi.org/10.1016/j.catena.2018.01.012openalex-extracted
GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method
inferredcites →https://doi.org/10.1016/j.catena.2018.03.003openalex-extracted
Review on landslide susceptibility mapping using support vector machines
inferredcites →https://doi.org/10.1016/j.catena.2018.12.013openalex-extracted
Susceptibility assessment of landslides triggered by earthquakes in the Western Sichuan Plateau
inferredcites →https://doi.org/10.1016/j.earscirev.2012.02.001openalex-extracted
Landslide inventory maps: New tools for an old problem
inferredcites →https://doi.org/10.1016/j.enggeo.2020.105972openalex-extracted
Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: A comparison between logistic regression and random forest
inferredcites →https://doi.org/10.1016/j.enggeo.2021.106051openalex-extracted
Geohazards and human settlements: Lessons learned from multiple relocation events in Badong, China – Engineering geologist's perspective
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Estimating the quality of landslide susceptibility models
inferredcites →https://doi.org/10.1016/j.geomorph.2009.09.025openalex-extracted
GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China
inferredcites →https://doi.org/10.1016/j.geomorph.2017.04.002openalex-extracted
A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the Wuyuan area, China
inferredcites →https://doi.org/10.1016/j.geomorph.2018.10.022openalex-extracted
A global slope unit-based method for the near real-time prediction of earthquake-induced landslides
inferredcites →https://doi.org/10.1016/j.ijdrr.2020.101498openalex-extracted
Crowd or Hubs: information diffusion patterns in online social networks in disasters
inferredcites →https://doi.org/10.1016/j.jhydrol.2019.03.073openalex-extracted
A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods
inferredcites →https://doi.org/10.1016/j.jseaes.2012.12.014openalex-extracted
A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey
inferredcites →https://doi.org/10.1016/j.rse.2011.05.013openalex-extracted
Object-oriented mapping of landslides using Random Forests
inferredcites →https://doi.org/10.1016/j.scitotenv.2018.10.064openalex-extracted
An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines
inferredcites →https://doi.org/10.1016/j.scitotenv.2019.01.221openalex-extracted
Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan
inferredcites →https://doi.org/10.1016/s0169-555x(97)00064-0openalex-extracted
An automated approach to the classification of the slope units using digital data
inferredcites →https://doi.org/10.1038/s41598-019-48773-2openalex-extracted
Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques
inferredcites →https://doi.org/10.1080/10106049.2017.1404141openalex-extracted
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/10.3390/app9132630openalex-extracted
A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning
inferredcites →https://doi.org/10.3390/app9183664openalex-extracted
Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models
inferredcites →https://doi.org/10.3390/f10090743openalex-extracted
New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed
inferredcites →https://doi.org/10.3390/ijgi9030144openalex-extracted
Spatial Prediction of Landslide Susceptibility Based on GIS and Discriminant Functions
inferredcites →https://doi.org/10.3390/rs12030502openalex-extracted
Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models
inferredcites →https://doi.org/10.3390/rs12091486openalex-extracted
Integration of Remotely Sensed Soil Sealing Data in Landslide Susceptibility Mapping
inferredcites →https://doi.org/10.3390/s19183940openalex-extracted
A Novel Performance Assessment Approach Using Photogrammetric Techniques for Landslide Susceptibility Mapping with Logistic Regression, ANN and Random Forest
inferredcites →https://doi.org/10.3390/s20061576openalex-extracted
Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network
inferredcites →https://doi.org/10.3390/su11164386openalex-extracted
Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms
inferredcites →https://doi.org/10.3390/su11226323openalex-extracted
A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping
inferredcites →https://doi.org/10.5194/nhess-13-2815-2013openalex-extracted
Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues
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