# Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure
## 4 Conclusions  
In the past, many scholars have investigated the slope failure phenomenon due to its huge impact on many civil engineering projects. Due to this, many traditional techniques offering numerical and finite element solutions have been developed. The appearance of machine learning has made these techniques antiquated. Therefore, the main incentive of this study was to evaluate the proficiency of various machine-learning-based 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), in estimating the factor of safety (FOS) of a cohesive slope. To provide the required dataset. Optum G2 software was effectively used to calculate the FOS of 630 different slope conditions. The inputs (i.e., the slope failure conditioning factors) chosen were the undrained shear strength (Cu), slope angle (\(\beta\)), setback distance ratio (b/B), and applied surcharge on the shallow foundation installed over the slope (w). To train the intelligent models, 80% of the provided dataset (i.e., 504 samples) was randomly selected. Then, the results of each model were validated using the remaining 20% (i.e., 126 samples). Five well-known statistical indices, namely coefficient of determination (R\({}^{2}\)), mean absolute error (_MAE_), root mean square error (_RMSE_), relative absolute error (_RAE_ in %), and root relative squared error (_RRSE_ in %), were used to evaluate the results of the MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT predictive models. A color intensity rating along with the total ranking method (i.e., based on the result of the above indices) was also developed. In addition to this, the results were compared by classifying the risk of slope failure using five classes: unstable, low stability (dangerous), moderate stability, good stability, and safe. The outcomes of this paper are as follows: \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Neuron (i)**} & \multicolumn{8}{c}{**Z\({}_{1}\) = Tansig (\(W_{11}\times Y_{1}+W_{2}\times Y_{2}+\ldots+W_{10}\times Y_{10}+b_{1}\))**} \\ \cline{2-10}  & \(W_{11}\) & \(W_{22}\) & \(W_{31}\) & \(W_{41}\) & \(W_{55}\) & \(W_{66}\) & \(W_{72}\) & \(W_{88}\) & \(W_{99}\) & \(W_{110}\) & \(b_{1}\) \\ \hline
1 & 0.8014 & 0.2141 & \(-\)0.9739 & 0.3018 & \(-\)1.0577 & 0.3676 & 0.0819 & 0.0814 & 0.1832 & \(-\)0.2797 & \(-\)0.1420 \\
2 & 0.7150 & \(-\)1.0819 & \(-\)1.6119 & 1.4173 & \(-\)1.7753 & \(-\)0.6882 & \(-\)0.3629 & \(-\)0.3738 & 0.3738 & 3.8024 & \(-\)0.8470 \\
3 & 0.0539 & \(-\)0.0413 & \(-\)0.0124 & \(-\)0.01376 & \(-\)1.5404 & \(-\)0.1659 & \(-\)0.1569 & 0.065 & 0.1123 & \(-\)1.0206 & 0.7256 \\
4 & 0.0975 & 0.2092 & \(-\)0.2620 & \(-\)0.0167 & \(-\)0.2081 & 0.1904 & 0.0662 & 0.0342 & \(-\)0.0256 & 1.3529 & 0.6269 \\
5 & 0.3407 & \(-\)0.2203 & \(-\)0.6002 & 0.7437 & \(-\)0.0743 & 0.2708 & 0.6381 & \(-\)0.04040 & \(-\)0.2663 & 1.6380 & 0.0664 \\
6 & 0.2808 & \(-\)1.1512 & 1.6090 & 0.0571 & \(-\)0.9023 & 1.0409 & \(-\)0.1826 & 1.3500 & \(-\)0.0688 & \(-\)0.6791 & \(-\)0.8171 \\
7 & \(-\)1.4209 & \(-\)1.3960 & \(-\)0.6175 & 0.0115 & 0.4415 & 1.3083 & \(-\)0.3546 & 1.1381 & \(-\)0.5560 & \(-\)1.3212 & \(-\)0.4100 \\
8 & \(-\)0.8622 & \(-\)1.4606 & 1.0247 & \(-\)1.4288 & \(-\)0.0929 & 0.8496 & 0.1114 & 0.1647 & \(-\)0.4303 & \(-\)0.4968 & \(-\)0.1472 \\
9 & 0.0532 & \(-\)0.0747 & \(-\)0.5253 & 0.0773 & \(-\)1.0538 & \(-\)0.7170 & \(-\)0.1178 & \(-\)0.0304 & 0.0095 & \(-\)0.7900 & \(-\)1.2037 \\
10 & 1.6879 & \(-\)1.7526 & \(-\)0.5477 & 2.4484 & \(-\)0.1138 & \(-\)1.9688 & 0.5682 & 2.5085 & \(-\)1.7107 & \(-\)0.5766 & \(-\)0.5541 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Weight and biases of the ANN model.