# Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure
## 2 Data Collection and Methodology
** & **C\({}_{a}\)** & \(\beta\) & **b/B** & **w** & **FOS** & **Stability Description** & **No. ** & **C\({}_{a}\)** & \(\beta\) & **b/B** & **w** & **FOS** & **Stability Description** \\ \hline
1 & 25 & 15 & 0 & 50 & 1.872 & Moderate & 26 & 100 & 60 & 1 & 150 & 2.682 & High \\
2 & 25 & 15 & 0 & 100 & 1.196 & Low & 27 & 100 & 60 & 2 & 50 & 4.671 & Very high \\
3 & 25 & 15 & 0 & 150 & 0.8154 & Unstable & 28 & 100 & 60 & 2 & 100 & 3.674 & High \\
4 & 25 & 15 & 1 & 50 & 1.852 & Moderate & 29 & 100 & 60 & 2 & 150 & 2.904 & High \\
5 & 25 & 15 & 1 & 100 & 1.324 & Low & 30 & 100 & 60 & 3 & 50 & 4.72 & Very high \\
6 & 25 & 15 & 1 & 150 & 0.8981 & Unstable & 31 & 100 & 60 & 3 & 100 & 3.753 & High \\
7 & 25 & 15 & 2 & 50 & 1.1834 & Moderate & 32 & 100 & 60 & 3 & 150 & 3.017 & High \\
8 & 25 & 15 & 2 & 100 & 1.354 & Low & 33 & 100 & 60 & 4 & 50 & 4.833 & Very high \\
9 & 25 & 15 & 2 & 150 & 0.9133 & Unstable & 34 & 100 & 60 & 4 & 100 & 3.904 & High \\
10 & 25 & 15 & 3 & 50 & 1.824 & Moderate & 35 & 100 & 60 & 4 & 150 & 3.152 & High \\
11 & 25 & 15 & 3 & 100 & 1.356 & Low & 36 & 100 & 60 & 5 & 50 & 4.996 & Very high \\
12 & 25 & 15 & 3 & 150 & 0.9169 & Unstable & 37 & 100 & 60 & 5 & 100 & 4.059 & Very high \\
13 & 25 & 15 & 4 & 50 & 1.825 & Moderate & 38 & 100 & 60 & 5 & 150 & 3.297 & High \\
14 & 25 & 15 & 4 & 100 & 1.356 & Low & 39 & 100 & 75 & 0 & 50 & 4.227 & Very high \\
15 & 25 & 15 & 4 & 150 & 0.9171 & Unstable & 40 & 100 & 75 & 0 & 100 & 2.425 & High \\
16 & 25 & 15 & 5 & 50 & 1.832 & Moderate & 41 & 100 & 75 & 0 & 150 & 1.68 & Moderate \\
17 & 25 & 15 & 5 & 100 & 1.357 & Low & 42 & 100 & 75 & 1 & 50 & 4.154 & Very high \\
18 & 25 & 15 & 5 & 150 & 0.9179 & Unstable & 43 & 100 & 75 & 1 & 100 & 3.174 & High \\
19 & 25 & 30 & 0 & 50 & 1.597 & Moderate & 44 & 100 & 75 & 1 & 150 & 2.344 & High \\
20 & 25 & 30 & 0 & 100 & 1.035 & Low & 45 & 100 & 75 & 2 & 50 & 4.065 & Very high \\
21 & 25 & 30 & 0 & 150 & 0.8 & Unstable & 46 & 100 & 75 & 2 & 100 & 3.185 & High \\
22 & 25 & 30 & 1 & 50 & 1.581 & Moderate & 47 & 100 & 75 & 2 & 150 & 2.545 & High \\
23 & 25 & 30 & 1 & 100 & 1.229 & Low & 48 & 100 & 75 & 3 & 50 & 4.116 & Very high \\
24 & 25 & 30 & 1 & 150 & 0.844 In the following, the aim was to evaluate the competency of the applied models through a novel procedure based on the classification. In this sense, the actual values of the safety factor (i.e., obtained from the Optum G2 analysis) were stratified into five stability classes. The safety factor values varied from 0.8 to 28.55. Hence, the classification was carried out with respect to the extents given below (Table 2):  
As previously expressed, this study aimed to appraise the applicability of the seven most-employed machine learning models, 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), toward evaluating the stability of a cohesive slope. For this purpose, the factor of safety (FOS) of the slope was estimated using the above-mentioned methods. Four slope stability effective factors that were chosen for this study were the undrained shear strength (C\({}_{\text{u}}\)), slope angle (\(\beta\)), setback distance ratio (b/B), and applied surcharge on the footing installed over the slope (w). To prepare the input target dataset, 630 different stages were developed and analyzed in Optum G2 finite element software. As a result of this process, a FOS was derived for each performed stage. To create the required dataset, the values of C\({}_{\text{u}}\), \(\beta\), b/B, and \(w\) were listed with the corresponding FOS. In the following, 80% of the prepared dataset (training phase consisting of 504 samples) was randomly selected for training the utilized models (MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT), and the effectiveness of them was evaluated by means of the remaining 20% of the dataset (testing phase consisting of 126 samples).