# Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure
## 3 Results and Discussion
In addition, RBFR outperformed MLP, MLR, and SMO-SVM (as shown in Table 3). The results for evaluating the performance of the models used are available in Table 4. Despite this fact that IBK showed an excellent performance during the training phase, a better validation was achieved using the RF, RBFR, RT, and MLP methods in this part. This claim can be proved by the obtained values of R\({}^{2}\) (0.9649, 0.9939, 0.9955, 0.9653, 0.9837, 0.9985, and 0.9950), _MAE_ (1.1939, 0.5149, 0.3936, 1.0360, 0.8184, 0.2152, and 0.3492) and _RAE_ (24.1272%, 10.4047%, 7.9549%, 20.9366%, 16.5388%, 4.3498%, and 7.0576%) for MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT, respectively. Respective values of 0.9985, 0.2152, 0.3312, 4.3498%, and 5.5145% computed for R\({}^{2}\), _MAE_, _RMSE_, _RAE_, and _RRSE_ indices show that the highest level of accuracy in the validation phase was achieved by RF. Similar to the training phase, the lowest reliability was achieved by the SVM and MLR models, compared to the other utilized techniques. Furthermore, a comprehensive comparison (regarding the results of both the training and testing datasets) of applied methods is conducted in Table 5. In this table, considering the assumption of individual ranks obtained for each model (based on the R\({}^{2}\), _MAE_, _RMSE_, _RAE_, and _RRSE_ in Tables 3 and 4), a new total ranking is provided. According to this table, the RF (total score = 60) was the most efficient model in the current study due to its good performance in both the training and testing phases. After RF, RT (total score = 57) was the second-most reliable model. Other employed methods including IBK (total score = 50), RBFR (total score = 48), and MLP (total score = 35) were the third-, fourth-, and fifth-most accurate tools, respectively. Also, the poorest estimation was given by the SMO-SVM and MLR approaches (total score = 15 for both models). Application of 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) was found to be viable for appraising the stability of a single-layered cohesive slope. The models that use ensemble learning (i.e., RF and RT) outperformed the models that are based on lazy-learning (IBK), regression rules (MLR and RBFR), neural learning (MLP), and statistical theories (SMO-SVM). Referring to the acquired total scores (the summation of scores obtained for the training and testing stages) of 15, 35, 48, 15, 50, 60, and 57 for MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT, respectively, shows the superiority of the RT algorithm. After that, RT, IBK, and RBFR were shown to have an excellent performance when estimating the FOS. Also, satisfying reliability was found for the MLP, SMO-SVM, and MLR methods. Based on the results obtained for the classification of the risk of failure,  
\begin{table}
\begin{tabular}{c|c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Proposed**} & \multicolumn{8}{c}{**Network Result**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-11}  & & \multicolumn{4}{c}{**Training Dataset**} & \multicolumn{4}{c}{**Testing Dataset**} & \multirow{2}{*}{**Score**} \\ \cline{2-2} \cline{4-11}  & **R\({}^{2}\)** & & & & & & & & & **Rank** \\ \hline MLR & 2 & 1 & 2 & 1 & 2 & 1 & 1 & 2 & 1 & 2 & 15 & 6 \\ MLP & 3 & 3 & 3 & 3 & 3 & 4 & 4 & 4 & 4 & 4 & 35 & 5 \\ RBFR & **4** & **4** & **4** & **4** & **4** & **6** & **5** & **6** & **5** & **6  
the closest approximation was achieved using the RF, RT, IBK, and SMO-SVM methods. In this regard, 14.60% of the dataset was assorted as hazardous slopes (i.e., \"unstable\" and \"low stability\" categories). This value was estimated as 18.25%, 12.54%, 12.70%, 14.76%, 14.60%, 13.81%, and 14.13% by the MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT models, respectively.