ArticleScientific reports2024
Predictive slope stability early warning model based on CatBoost.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
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- Development and internal validation of a machine learning-based prediction model for pulmonary hypertension in COPD.Frontiers in medicine · 2026Article
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- Hybrid Radiomics and Machine Learning for Brain Tumors Multi-Task Classification: An Exploratory Study on Integrating GLCM and Curvelet-Based Features for Enhanced Accuracy.Health science reports · 2025Article
- Machine learning-based identification of key factors and spatial heterogeneity analysis of urban flooding: a case study of the central urban area of Ordos.Scientific reports · 2025Article
- Research on subway settlement prediction based on the WTD-PSR combination and GSM-SVR model.Scientific reports · 2025Article
- Predicting CaO activity in multiple slag system using improved whale optimization algorithm and categorical boosting.Scientific reports · 2025Article
- Random forest-based prediction of shallow slope stability considering spatiotemporal variations in unsaturated soil moisture.Scientific reports · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
A model for predicting slope stability is developed using Categorical Boosting (CatBoost), which incorporates 6 slope features to characterize the state of slope stability. The model is trained using a symmetric tree as the base model, utilizing ordered boosting to replace gradient estimation, which enhances prediction accuracy. Comparative models including Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Logistic Regression (LR) were introduced. Five performance evaluation metrics are utilized to assess the predictive capabilities of the CatBoost model. Based on CatBoost model, the predicted probability of slope instability is calculated, and the early warning model of slope instability is further established. The results suggest that the CatBoost model demonstrates a 6.25% disparity in accuracy between the training and testing sets, achieving a precision of 100% and an Area Under Curve (AUC) value of 0.95. This indicates a high level of predictive accuracy and robust ordering capabilities, effectively mitigating the problem of overfitting. The slope instability warning model offers reasonable classifications for warning levels, providing valuable insights for both research and practical applications in the prediction of slope stability and instability warning.
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