# Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure
## 3 Results and Discussion  
The calculated values of R\({}^{2}\), _MAE_, _RMSE_, _RAE_, and _RRSE_ for estimating the FOS are tabulated in Tables 3 and 4 for the training and testing datasets. Also, the total ranking obtained for the models is featured in Table 5. A color intensity model is given to denote the quality of results graphically. A red collection is considered for particular results. A higher value of R\({}^{2}\) and lower _MAE_, _RMSE_, _RAE_, and _RRSE_ are represented using a more intense red color. In the last column, the intensity of a green collection is supposed to be inversely proportional to the ranking gained by each model. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Proposed**} & \multicolumn{8}{c}{**Network Results**} & \multicolumn{8}{c}{**Ranking the Predicted Models**} & \multicolumn{2}{c}{**Total Ranking**} \\ \cline{2-13} \cline{6-13}
**Models** & **R\({}^{2}\)** & **MAE** & **RMSE** & **RAE (\%)** & **RMSE** (\%)** & **R\({}^{2}\)** & **MAE** & **RMSE** & **RAE (\%)** & **RMSE** (\%)** & **Score** & **Rank** \\ \hline MLR & 0.9649 & 1.1939 & 1.5891 & 24.1272 & 26.4613 & 1 & 1 & 2 & 1 & 2 & 7 & 7 \\ MLP & 0.9993 & 0.5194 & 0.7036 & 10.0404 & 11.8116 & 4 & 4 & 4 & 4 & 4 & 20 & 4 \\ RERR & 0.9995 & 0.5036 & 0.5667 & 7.9549 & 0.4376 & 6 & 5 & 6 & 5 & 6 & 28 & 2 \\ SMO-SVM & 0.9653 & 1.0360 & 1.6362 & 20.9366 & 27.247 & 2 & 2 & 1 & 2 & 1 & 8 & 6 \\ IRK & 0.9837 & 0.8184 & 1.10166 & 16.5388 & 17.247 & 3 & 3 & 3 & 3 & 3 & 15 & 5 \\ RF & 0.9985 & 0.2152 & 0.93812 & 4.3498 & 5.5145 & 7 & 7 & 7 & 7 & 2 & 35 & 1 \\ RT & 0.9950 & 0.3492 & 0.6033 & 7.0556 & 10.047 & 5 & 6 & 5 & 6 & 5 & 27 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Total ranking of the testing dataset in predicting the factor of safety. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Proposed**} & \multicolumn{8}{c}{**Network Results**} & \multicolumn{8}{c}{**Ranking the Predicted Models**} & \multicolumn{2}{c}{**Total Ranking**} \\ \cline{2-13}
**Models** & **R\({}^{2}\)** & **MAE** & **RMSE** & **RAE (\%)** & **RMSE** (\%)** & **R\({}^{2}\)** & **MAE** & **RMSE** & **RAE (\%)** & **RMSE** (\%)** & **Score** & **Rank** \\ \hline MLR & 0.9586 & 1.2527 & 1.7366 & 25.0515 & 28.4887 & 2 & 1 & 2 & 1 & 2 & 8 & 6 \\ MLP & 0.9993 & 0.494 & 0.7131 & 9.8726 & 11.6985 & 3 & 3 & 3 & 3 & 3 & 15 & 5 \\ RERR & 0.9948 & 0.93976 & 0.6231 & 7.9522 & 10.2221 & 4 & 4 & 4 & 4 & 4 & 20 & 4 \\ SMO-SVM & 0.9529 & 1.161 & 1.9183 & 23.2182 & 31.4703 & 1 & 2 & 1 & 2 & 1 & 7 & 7 \\ IBK & 1.0000 & 0 & 0 & 0 & 0 & 0 & 7 & 7 & 7 & 7 & 7 & 35 & 1 \\ RF & 0.9997 & 0.1063 & 0.14886 & 2.2154 & 2.4388 & 5 & 5 & 5 & 5 & 5 & 25 & 3 \\ RT & 0.9998 & 0.0811 & 0.1111 & 1.622 & 1.8221 & 6 & 6 & 6 & 6 & 6 & 30 & 2 \\ \hline \hline \end{tabular} Note: It is a ranking system based on cultures. The one with higher intensity has better value. R\({}^{2}\): correlation coefficient; \(MAE\): mean absolute error; \(RMSE\): root mean squared error; \(RAE\): relative absolute error; \(RMSE\): root relative squared error.  
\end{table} 
Table 3: Total ranking of the training dataset in predicting the factor of safety. At a glance, RF could be seen to be the outstanding model due to its highest score. However, based on the acquired R\({}^{2}\) for the training (0.9586, 0.9937, 0.9948, 0.9529, 1.000, 0.9997, and 0.9998, calculated for MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT, respectively) and testing (0.9649, 0.9939, 0.9955, 0.9653, 0.9837, 0.9985, and 0.9950, respectively) datasets, an acceptable correlation could be seen for all models. Referring to the _RMSE_ (28.4887, 1.7366, 0.7131, 0.6231, 1.9183, 0.00, 0.1486, and 0.1111) and _RMSE_ (11.6985%, 10.2221%, 31.4703%, 0.0000%, 2.4385%, and 1.8221%) obtained for the training dataset, the IBK predictive model presented the most effective training compared to other models. Also, RT and RF could be seen as the second- and third-most accurate models, respectively, in the training stage.