Strategy of oversampling geotechnical parameters through geostatistical, SMOTE, and CTGAN methods for assessing susceptibility of landslide
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Strategy of oversampling geotechnical parameters through geostatistical, SMOTE, and CTGAN methods for assessing susceptibility of landslide
Authors
Keywords
-
Journal
Landslides
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2023-10-19
DOI
10.1007/s10346-023-02166-9
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Geometric SMOTE for regression
- (2022) Luís Camacho et al. EXPERT SYSTEMS WITH APPLICATIONS
- A GIS-based tool for probabilistic physical modelling and prediction of landslides: GIS-FORM landslide susceptibility analysis in seismic areas
- (2022) Jian Ji et al. Landslides
- An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost
- (2022) Xinzhi Zhou et al. Geocarto International
- SVNN-ANFIS approach for stability evaluation of open-pit mine slopes
- (2022) Jibo Qin et al. EXPERT SYSTEMS WITH APPLICATIONS
- Creating large scale probabilistic boundaries using Gaussian Processes
- (2022) Adrian Ball et al. EXPERT SYSTEMS WITH APPLICATIONS
- Probabilistic prediction of rock avalanche runout using a numerical model
- (2022) Jordan Aaron et al. Landslides
- Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study
- (2022) Junwei Ma et al. Landslides
- Rapid prediction of landslide dam stability considering the missing data using XGBoost algorithm
- (2022) Ning Shi et al. Landslides
- Topological mapping of complex networks from high slope deformation time series for landslide risk assessment
- (2022) Yuanwen Han et al. EXPERT SYSTEMS WITH APPLICATIONS
- A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset
- (2022) Husam A.H. Al-Najjar et al. GONDWANA RESEARCH
- Handling data imbalance in machine learning based landslide susceptibility mapping: a case study of Mandakini River Basin, North-Western Himalayas
- (2022) Sharad Kumar Gupta et al. Landslides
- Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning
- (2021) Justin Engelmann et al. EXPERT SYSTEMS WITH APPLICATIONS
- Application of deep learning algorithms in geotechnical engineering: a short critical review
- (2021) Wengang Zhang et al. ARTIFICIAL INTELLIGENCE REVIEW
- Discontinuity Predictions of Porosity and Hydraulic Conductivity Based on Electrical Resistivity in Slopes through Deep Learning Algorithms
- (2021) Seung-Jae Lee et al. SENSORS
- Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping
- (2021) Dae-Hong Min et al. Scientific Reports
- Classification of imbalanced hyperspectral images using SMOTE-based deep learning methods
- (2021) Akın Özdemir et al. EXPERT SYSTEMS WITH APPLICATIONS
- Probabilistic analysis of the active earth pressure on earth retaining walls for c-ϕ soils according to the Mazindrani and Ganjali method
- (2021) Julian Osorio et al. International Journal of Geo-Engineering
- Numerical study for optimal design of soil nailed embankment slopes
- (2021) Rabah Derghoum et al. International Journal of Geo-Engineering
- Stability analysis for two-layered slopes by using the strength reduction method
- (2021) Sourav Sarkar et al. International Journal of Geo-Engineering
- Providing a greater precision of Situational Awareness of urban floods through Multimodal Fusion
- (2021) Thiago Aparecido Gonçalves da Costa et al. EXPERT SYSTEMS WITH APPLICATIONS
- A New Integrated Approach for Landslide Data Balancing and Spatial Prediction Based on Generative Adversarial Networks (GAN)
- (2021) Husam A. H. Al-Najjar et al. Remote Sensing
- Generative adversarial network for road damage detection
- (2020) Hiroya Maeda et al. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
- Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks
- (2020) Husam.A.H. Al-Najjar et al. Geoscience Frontiers
- A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique
- (2020) Feng Shen et al. APPLIED SOFT COMPUTING
- Machine learning for landslides prevention: a survey
- (2020) Zhengjing Ma et al. NEURAL COMPUTING & APPLICATIONS
- Sensitivities of input parameters for predicting stability of soil slope
- (2019) Hyunwook Choo et al. Bulletin of Engineering Geology and the Environment
- Learning imbalanced datasets based on SMOTE and Gaussian distribution
- (2019) Tingting Pan et al. INFORMATION SCIENCES
- A hybrid localization model using node segmentation and improved particle swarm optimization with obstacle-awareness for wireless sensor networks
- (2019) Songyut Phoemphon et al. EXPERT SYSTEMS WITH APPLICATIONS
- Estimation of elastic wave velocity and DCPI distributions using outlier analysis
- (2018) Saheed Mayowa Taiwo et al. ENGINEERING GEOLOGY
- A comparison of multitask and single task learning with artificial neural networks for yield curve forecasting
- (2018) Manuel Nunes et al. EXPERT SYSTEMS WITH APPLICATIONS
- Weighted-SMOTE: A modification to SMOTE for event classification in sodium cooled fast reactors
- (2017) Manas Ranjan Prusty et al. PROGRESS IN NUCLEAR ENERGY
- Characterization of alluvium soil using geophysical and sounding methods
- (2015) Chung-Hwa Park et al. MARINE GEORESOURCES & GEOTECHNOLOGY
- To combat multi-class imbalanced problems by means of over-sampling and boosting techniques
- (2014) Lida Abdi et al. SOFT COMPUTING
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationPublish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn More