## 4 Result and Performance Assessment of Models  
### Errors and Other Parameters  
For measuring the extent of perfection of the model, different errors and values are computed. Assessing the performance of different models on different scales gives an overview of how the model performed, and it becomes facile for a researcher, a programmer, or a reader to compare the models based on their results. In this study, Root Mean  
Figure 1: Considered retaining wall. Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), Bias Factor, Nash-Sutcliffe Efficiency (NS), Variance Account Factor (VAF), Coefficient of Determination (R\({}^{2}\)), Adjusted Determination Coefficient (ADJ. R\({}^{2}\)), Performance Index (PI), RMSE To Standard Deviation Ratio (RSR), Normalized Mean Bias Factor (NMBE), Mean Absolute Percentage Error (MAPE), Wilmott's Index (WI), Legate and McCabe's Index (LMI), Expanded Uncertainty (\(U_{95}\)), t-Statistic, Global Performance Indicator (GPI), Correlation Coefficient (R), and Weighted Mean Absolute Percentage Error (WMAPE) are computed for each model. Table 1 shows the values of the errors as mentioned earlier and parameters. These parameters define the trend and relation between the predicted and observed values. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**MODELS** & **MARS** & **EmNN** & **SOS-LSSVM** \\ \hline
1. WMAPE & 0.0062 & 0.0726 & 0.0008 \\ \cline{2-4} RANK & 2.0000 & 1.0000 & 3.0000 \\ \hline
2. NS & 0.9999 & 0.9860 & 1.0000 \\ \cline{2-4} RANK & 2.0000 & 1.0000 & 3.0000 \\ \hline
3. RMSE & 0.0017 & 0.0183 & 0.0002 \\ \cline{2-4} RANK & 2.0000 & 1.0000 & 3.0000 \\ \hline
4. VAF & 99.9914 & 98.7306 & 99.9998 \\ \cline{2-4} RANK & 2.0000 & 1.0000 & 3.0000 \\ \hline
5. R\({}^{2}\) & 0.9999 & 0.9860 & 1.0000 \\ \cline{2-4} RANK & 2.0000 & 1.0000 & 3.0000 \\ \hline
6. Adj. R\({}^{2}\) & 0.9997 & 0.9674 & 1.0000 \\ \cline{2-4} RANK & 2.0000 & 1.0000 & 3.0000 \\ \hline
7. PI & 1.9980 & 1.9364 & 1.9998 \\ \cline{2-4} RANK & 2.0000 & 1.0000 & 3.0000 \\ \hline
8. RMSD & 0.0017 & 0.0183 & 0.0002 \\ \cline{2-4} RANK & 2.0000 & 1.0000 & 3.0000 \\ \hline
9. BIAS FACTOR & 0.8729 & 0.8371 & 0.8755 \\ \cline{2-4} RANK & 2.0000 & 1.0000 & 3.0000 \\ \hline
10. RSR & 0.0106 & 0.1182 & 0.0015 \\ \cline{2-4} RANK & 2.0000 & 1.0000 & 3.0000 \\ \hline
11. NMBE & \(-\)0.3611 & 2.7843 & 0.0196 \\ \cline{2-4} RANK & 2.0000 & 1.0000 & 3.0000 \\ \hline
12. MAPE & 0.0010 & 0.0062 & 0.0000 \\ \cline{2-4} RANK & 2.0000 & 1.0000 & 3.0000 \\ \hline
13. WI & 1.0000 & 0.9966 & 1.0000 \\ \cline{2-4} RANK & 2.0000 & 1.0000 & 3.0000 \\ \hline
14. MAE & 0.0013 & 0.0155 & 0.0002 \\ \cline{2-4} RANK & 2.0000 & 1.0000 & 3.0000 \\ \hline
15. MBE & \(-\)0.0008 & 0.0059 & 0.0000 \\ \cline{2-4} RANK & 2.0000 & 1.0000 & 3.0000 \\ \hline
16. LMI & 0.9859 & 0.8364 & 0.9984 \\ \cline{2-4} RANK & 2.0000 & 1.0000 & 3.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance evaluation of applied methods via various error measurements (continued). The Scatter Index (SI) employed in this study is a statistical quantity used for measuring the error in the predicted value [41]. For computing, this value RMSE is divided by the mean of observed values. This quantity indicates if the values forecasted by the models are up to the mark or not. The \(a20\)-index is an engineering index used to assess the system's reliability [42]. It is calculated as follows:  
\[a20-index=\frac{m20}{M}. \tag{8}\]  
where \(m20\) is the number of data whose value is the ratio of observed FOS to predicted FOS falling between 0.8 and 1.20, and \(M\) is the total number of datasets available. This parameter's physical engineering significance gives the predicted values that deviate by \(\pm 20\%\) from the experimental values. Total rank is also calculated in this paper based on what is presented in [43]. After calculating all the parameters mentioned above, models are ranked accordingly.