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Prediction of slope stability using Tree Augmented Naive-Bayes classifier: modeling and performance evaluation
Feezan Ahmad1, Xiao-Wei Tang1, Jiang-Nan Qiu2
1State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China.
Mathematical Biosciences and Engineering : MBE
|April 18, 2022
Summary
A new Tree Augmented Naive-Bayes (TAN) model accurately predicts slope stability, outperforming existing empirical models. Slope height is identified as the most critical factor influencing stability predictions.
Area of Science:
- Geotechnical Engineering
- Machine Learning Applications
- Natural Hazard Assessment
Background:
- Slope stability prediction is crucial for landslide risk mitigation.
- Complex, multi-factorial relationships often hinder mathematical characterization of slope stability.
Purpose of the Study:
- To develop a novel Tree Augmented Naive-Bayes (TAN) model for predicting slope stability.
- To evaluate the TAN model's performance against established empirical methods.
Main Methods:
- A Tree Augmented Naive-Bayes (TAN) model was developed using six input factors: cohesion, internal friction angle, pore pressure ratio, slope angle, and unit weight.
- The model was trained and tested on 87 slope stability case records from published literature.
Main Results:
- The TAN model demonstrated superior performance in predicting slope stability compared to previous empirical models.
- Performance was assessed using accuracy, precision, recall, F-score, and Matthews correlation coefficient.
- Sensitivity analysis identified slope height as the most influential factor.
Conclusions:
- The developed TAN model offers an accurate and reliable method for predicting slope stability.
- The findings provide valuable insights for landslide hazard assessment and mitigation strategies.
Keywords:
Tree Augmented Naive-Bayesmachine learning algorithmsensitivity analysisslope stability predictionMore Related Videos
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