Predictive slope stability early warning model based on CatBoost
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 estimati...
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-77058-6 |
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| author | Yuan Cai Ying Yuan Aihong Zhou |
| author_facet | Yuan Cai Ying Yuan Aihong Zhou |
| author_sort | Yuan Cai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4db51c70349f4c22980144c853da1ef6 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4db51c70349f4c22980144c853da1ef62025-08-20T02:18:32ZengNature PortfolioScientific Reports2045-23222024-10-0114111410.1038/s41598-024-77058-6Predictive slope stability early warning model based on CatBoostYuan Cai0Ying Yuan1Aihong Zhou2School of Urban Geology and Engineering, Hebei GEO UniversityHebei Technology Innovation Center for Intelligent Development and Control of Underground Built EnvironmentHebei Technology Innovation Center for Intelligent Development and Control of Underground Built EnvironmentAbstract 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.https://doi.org/10.1038/s41598-024-77058-6Slope stabilityModel predictionCategorical boostingSlope warningGradient boosting decision tree |
| spellingShingle | Yuan Cai Ying Yuan Aihong Zhou Predictive slope stability early warning model based on CatBoost Scientific Reports Slope stability Model prediction Categorical boosting Slope warning Gradient boosting decision tree |
| title | Predictive slope stability early warning model based on CatBoost |
| title_full | Predictive slope stability early warning model based on CatBoost |
| title_fullStr | Predictive slope stability early warning model based on CatBoost |
| title_full_unstemmed | Predictive slope stability early warning model based on CatBoost |
| title_short | Predictive slope stability early warning model based on CatBoost |
| title_sort | predictive slope stability early warning model based on catboost |
| topic | Slope stability Model prediction Categorical boosting Slope warning Gradient boosting decision tree |
| url | https://doi.org/10.1038/s41598-024-77058-6 |
| work_keys_str_mv | AT yuancai predictiveslopestabilityearlywarningmodelbasedoncatboost AT yingyuan predictiveslopestabilityearlywarningmodelbasedoncatboost AT aihongzhou predictiveslopestabilityearlywarningmodelbasedoncatboost |