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

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ID: 112406
2019
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Abstract
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.
Reference Key
moayedi2019appliedmachine-learning-based Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hossein Moayedi;Dieu Tien Bui;Bahareh Kalantar;Loke Kok Foong;Moayedi, Hossein;Tien Bui, Dieu;Kalantar, Bahareh;Kok Foong, Loke;
Journal applied sciences
Year 2019
DOI
10.3390/app9214638
URL
https://www.mdpi.com/2076-3417/9/21/4638#citedby
Keywords
Machine learning classification slope stability failure recognition

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