Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure

In this paper, the authors investigated the applicability of combining machine-learning-based models toward slope stability assessment. To do this, several well-known machine-learning-based methods, namely multiple linear regression (MLR), multi-layer perceptron (MLP), radial basis function regressi...

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Bibliographic Details
Published in:Applied sciences 2019-11, Vol.9 (21), p.4638
Main Authors: Moayedi, Hossein, Tien Bui, Dieu, Kalantar, Bahareh, Kok Foong, Loke
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Classification
Datasets
Failure
failure recognition
Learning algorithms
machine learning
Multilayers
Optimization
Radial basis function
Regression analysis
Shear strength
Slope stability
Software
Stability analysis
Statistical analysis
Support vector machines
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Summary:In this paper, the authors investigated the applicability of combining machine-learning-based models toward slope stability assessment. To do this, several well-known machine-learning-based methods, namely multiple linear regression (MLR), multi-layer perceptron (MLP), radial basis function regression (RBFR), improved support vector machine using sequential minimal optimization algorithm (SMO-SVM), lazy k-nearest neighbor (IBK), random forest (RF), and random tree (RT), were selected to evaluate the stability of a slope through estimating the factor of safety (FOS). In the following, a comparative classification was carried out based on the five stability categories. Based on the respective values of total scores (the summation of scores obtained for the training and testing stages) of 15, 35, 48, 15, 50, 60, and 57, acquired for MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT, respectively, it was concluded that RF outperformed other intelligent models. The results of statistical indexes also prove the excellent prediction from the optimized structure of the ANN and RF techniques.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9214638