Prediction of slope stability using Tree Augmented Naive-Bayes classifier: modeling and performance evaluation

Feezan Ahmad; Xiao-Wei Tang; Jiang-Nan Qiu; Piotr Wróblewski; Mahmood Ahmad; Irfan Jamil; Feezan Ahmad; Xiao-Wei Tang; Jiang-Nan Qiu; Piotr Wróblewski; Mahmood Ahmad; Irfan Jamil

doi:10.3934/mbe.2022209

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 5: 4526-4546. doi: 10.3934/mbe.2022209

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Prediction of slope stability using Tree Augmented Naive-Bayes classifier: modeling and performance evaluation

1.
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China
2.
Faculty of Management and Economics, Dalian University of Technology, Dalian 116024, China
3.
Faculty of Engineering, University of Technology and Economics H. Chodkowska in Warsaw, Jutrzenki 135, 02-231 Warsaw, Poland
4.
Faculty of Mechatronics, Armament and Aerospace of the Military University of Technology, Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
5.
Department of Civil Engineering, Faculty of Engineering, International Islamic University Malaysia, Jalan Gombak, Selangor 50728, Malaysia
6.
Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu 28100, Pakistan
7.
Department of Civil Engineering, University of Engineering and Technology Peshawar, Peshawar 25120, Pakistan

Received: 04 December 2021 Revised: 23 January 2022 Accepted: 15 February 2022 Published: 04 March 2022

Predicting slope stability is critical for identifying terrain that is prone to landslides and mitigating the damage caused by landslides. The relationships between factors that determine slope instability are complicated and multi-factorial, so it is sometimes difficult to mathematically characterize slope stability. In this paper, new Tree Augmented Naive-Bayes (TAN) model was developed to predict slope stability subjected to circular failures based on six input factors: cohesion, internal friction angle, pore pressure ratio, slope angle, unit weight, and slope angle. A total 87 slope stability case records obtained from published literature was used to train and test the proposed TAN model. According to the results of the performance indices—accuracy, precision, recall, F-score and Matthews correlation coefficient, the established TAN model was proven to be better at predicting slope stability with acceptable accuracy than other formerly developed empirical models in the literature. Furthermore, the slope height was revealed as the most sensitive factor in a sensitivity analysis.
- machine learning algorithm,
- Tree Augmented Naive-Bayes,
- sensitivity analysis,
- slope stability prediction
Citation: Feezan Ahmad, Xiao-Wei Tang, Jiang-Nan Qiu, Piotr Wróblewski, Mahmood Ahmad, Irfan Jamil. Prediction of slope stability using Tree Augmented Naive-Bayes classifier: modeling and performance evaluation[J]. Mathematical Biosciences and Engineering, 2022, 19(5): 4526-4546. doi: 10.3934/mbe.2022209

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Abstract

Predicting slope stability is critical for identifying terrain that is prone to landslides and mitigating the damage caused by landslides. The relationships between factors that determine slope instability are complicated and multi-factorial, so it is sometimes difficult to mathematically characterize slope stability. In this paper, new Tree Augmented Naive-Bayes (TAN) model was developed to predict slope stability subjected to circular failures based on six input factors: cohesion, internal friction angle, pore pressure ratio, slope angle, unit weight, and slope angle. A total 87 slope stability case records obtained from published literature was used to train and test the proposed TAN model. According to the results of the performance indices—accuracy, precision, recall, F-score and Matthews correlation coefficient, the established TAN model was proven to be better at predicting slope stability with acceptable accuracy than other formerly developed empirical models in the literature. Furthermore, the slope height was revealed as the most sensitive factor in a sensitivity analysis.

References

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