## 3 Results and Discussion
The magnitudes of correlation are shown in the form of the Taylor diagram that generates a more detailed appraisal of the model performances [101] for training and testing phases. The Taylor diagram illustrates a more tangible and convincing statistical relationship between the predicted and observed _Ss_ depending on correlation with respect to standard deviations. It is seen that the benchmark models ANFIS-DE and ANFIS-ACO are not appropriate in the training session, as the correlation to standard deviation points was highly parted from the ideal observed point as compared to ANFIS-PSO, ANFIS, and ANFIS-GA. The hybrid ANFIS-PSO model lay close to the perfect observed point in the testing phase more closely, followed by ANFIS-GA, ANFIS, ANFI-DE, and ANFIS-ACO models, which confirms that the prediction accuracy of ANFS-PSO was reasonably higher than the benchmark models. Figure 5: Boxplot of computed shear strength against predicted ones (**a**) Training stage and (**b**) Testing stage. To evaluate the trade-off between the accuracy and efficiency of the newly developed models, their computational time (CPU time) is presented in Figure 7.  
Figure 6: Normalized Taylor diagrams of predicted and observed standardized shear strength: (**a**) Training stage and (**b**) Testing stage. From Figure 7, it is evident that the lowest CPU time (853.23 s) is observed in ANFIS-PSO, while the ANFIS-DE offers the highest value (1272.06 s). The results confirm that the ANFIS-PSO model provides the highest performance prediction with most top convergence speed in comparison with other hybrid techniques. The uncertainties of model, variables, and data were evaluated on the basis of boxplots presentation based on the performance metrics over the training and testing phases at 25%, 50%, and 75% quantile together with IQR (Figure 8, 9, and 10). In the cases of the model's uncertainty based on performance metrics over the training and testing phases, the majority of the cases revealed a median value towards the 1 st quartile for both training and testing phases (Figure 8). However, in the case of the training set, all performance metrics exhibited marginal higher redundant than testing phase with the average values of IQR lies at 0.68 and 0.84, respectively. In cases of variable's uncertainty based on performance metrics over the training and testing phases were exhibited the distinguished characters (Figure 9); during the training phase, the median value tends towards the 3 rd quartile in most of the performance metrics. In contrast, it was mixed, tending towards the 1 st quartile (for \(RMSE\), \(CC\), and \(SRMSE\)), 2 nd quartile (for \(MAE\) and \(LMI\)), and 3 rd quartile (for \(WI\)) in the testing phase. In the cases of the data's uncertainty based on performance metrics over the training and testing phases were presented mixed characteristics such as the median line of boxplot tends towards 1 st quartile for \(RMSE\), \(MAE\) and \(LMI\), whereas \(CC\) and \(WI\) were opposite towards the 3 rd quartile but remained almost in the middle position in case of \(SRMSE\) (Figure 10). However, the testing phase has demonstrated the stability, which is near the middle for all performance metrics except \(WI\) and \(SRMSE\), which were towards the 1 st quartile. Figure 7: Comparison between computational time (CPU time) obtained from hybrid ANFIS models. Figure 8: Boxplot of the model’s uncertainty based on performance index over (**a**) Training stage and (**b**) Testing stage. Figure 9: Boxplot of variable’s uncertainty based on performances index over (**a**) Training stage and (**b**) Testing stage. The aptness of the hybrid and standalone ANFIS models using different input combinations (Model-1, Model-2... Model-18) to predict _Ss_ was explored in this paper.