# Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Neuron (i)**} & \multicolumn{2}{c}{**Y\({}_{1}\) = Tansig (\(W_{11}\times C_{1}+W_{12}\times\beta+W_{110}\times(b/B)+W_{12}\times w+b_{1}\))**} \\ \cline{2-6}  & \(W_{11}\) & \(W_{12}\) & \(W_{13}\) & \(W_{14}\) & \(b_{1}\) \\ \hline
1 & 1.1179 & 2.1216 & \(-\)0.9307 & \(-\)1.9126 & \(-\)3.1819 \\
2 & \(-\)1.6540 & 0.7670 & 0.5226 & 0.5905 & 1.9420 \\
3 & \(-\)1.7577 & 0.1682 & 1.0751 & \(-\)0.8448 & 1.5533 \\
4 & \(-\)1.1786 & \(-\)1.2008 & 1.5102 & \(-\)1.4182 & 1.2264 \\
5 & 0.3132 & 0.0403 & 0.0188 & \(-\)0.0171 & \(-\)0.0729 \\
6 & 0.3103 & \(-\)0.1702 & 0.8267 & \(-\)2.1844 & 0.1736 \\
7 & \(-\)0.2284 & \(-\)1.6277 & 1.7207 & 1.2254 & \(-\)0.0037 \\
8 & 1.9007 & \(-\)1.8777 & 0.9780 & 2.9271 & 1.4079 \\
9 & 0.1767 & \(-\)1.2933 & \(-\)1.2124 & \(-\)0.5631 & 1.4512 \\
10 & 0.5156 & \(-\)0.3139 & \(-\)0.0902 & \(-\)0.3256 & \(-\)1.2137 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Weight and biases of the ANN model. * 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) were viable for appraising the stability of a single-layered cohesive slope. * A good level of accuracy was achieved by all applied models based on the obtained values of R\({}^{2}\) (0.9586, 0.9937, 0.9948, 0.9529, 1.000, 0.9997, and 0.9998), _RMSE_ (28.4887, 1.7366, 0.7131, 0.6231, 1.9183, 0.0000, 0.1486, and 0.1111), and _RSSE_ (11.6985%, 10.2221%, 31.4703%, 0.0000%, 2.4385%, and 1.8221%) for the training dataset, and 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 the testing dataset obtained for MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT, respectively. * 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, the superiority of the RF algorithm can be seen. After that, RT, IBK, and RBFR showed an excellent performance in estimating the FOS. Also, satisfactory reliability was achieved for the MLP, SMO-SVM, and MLR methods. * Based on the results obtained for the classification of the risk of failure, the closest approximation was achieved by the RF, RT, IBK, and SMO-SVM methods. In this regard, 14.60% of the dataset was assorted as being 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. [PERSON] and B.K. performed experiments and field data collection; [PERSON] and [PERSON] wrote the manuscript, [PERSON] discussion and analyzed the data. B.K. and D.T.B. edited, restructured, and professionally optimized the manuscript. This work was financially supported by Ton Duc Thang University and University of South-East Norway. The authors declare no conflict of interest.