## 4 Discussion  
Use of IRI has received much attention as a crucial tool for analysis of pavement performance prediction and life cycle cost because IRI is considered as a quality assurance parameter in the procedure of construction [72] and reflects the ride quality and comfort level of passengers. Besides, roughness-based indices have been used as a practical and reliable method for predicting performance of pavement and analyzing maintenance and rehabilitation [73]. The health status of the pavement can be assessed by observing present distresses, checking the material properties and estimating quality of the construction [74]. As assessment of the performance of the road pavement structure based on IRI prediction is a very important and necessary task for efficient management of the road infrastructure, there are several studies on the relationship between IRI and pavement distress [75; 76; 77]. Determination of IRI is important task to quantify road surface roughness. On the other hand, the traditional methods to measure the IRI is time-consuming and cost a great fee. Thus, prediction of the IRI using advanced ML techniques is an effective solution which has not only a quick determination but also reduces significantly the cost of measurement. In the present study, we recommended potential ML methods PSOANFIS, GANFIS, FAANFIS, and ANN which have a better prediction capability for the IRI. It can be observed from the validation results of the models that the PSOANFIS, GANFIS, FAANFIS model have acceptable performance whereas ANN has a slightly poor capability for the prediction of the IRI. It can be stated that PSOANFIS has stable performance and the best prediction capability, \begin{table}
\begin{tabular}{c c c c c} \hline
**No** & **Pair Samples** & \(\mathbf{z}\)**-Value** & \(\mathbf{p}\)**-Value** & **Significance** \\ \hline
1 & PSOANFIS vs. GANFIS & 7.163 & \(<\)0.0001 & Yes \\
2 & PSOANFIS vs. FAANFIS & \(-\)2.793 & 0.0052 & Yes \\
3 & PSOANFIS vs. ANN & \(-\)6.692 & \(<\)0.0001 & Yes \\
4 & GANFIS vs. FAANFIS & \(-\)1.73 & 0.0836 & No \\
5 & GANFIS vs. ANN & \(-\)6.859 & \(<\)0.0001 & Yes \\
6 & FAANFIS vs. ANN & \(-\)6.135 & \(<\)0.0001 & Yes \\ \hline \end{tabular}
\end{table} 
Table 4: Non-parametric test using training dataset. Figure 8: Correlation results analysis of the models using the testing dataset: (**a**) PSOANFIS, (**b**) GANFIS, (**c**) FAANFIS, (**d**) ANN. \begin{table}
\begin{tabular}{c c c c c} \hline
**No** & **Pair Samples** & \(\mathbf{z}\)**-Value** & \(\mathbf{p}\)**-Value** & **Significance** \\ \hline
1 & PSOANFIS vs. GANFIS & 6.35 & \(<\)0.0001 & Yes \\
2 & PSOANFIS vs. FAANFIS & \(-\)3.997 & 0.0001 & Yes \\
3 & PSOANFIS vs. ANN & \(-\)5.597 & \(<\)0.0001 & Yes \\
4 & GANFIS vs. FAANFIS & \(-\)2.869 & 0.0041 & Yes \\
5 & GANFIS vs. ANN & \(-\)5.897 & \(<\)0.0001 & Yes \\
6 & FAANFIS vs. ANN & \(-\)4.2 & \(<\)0.0001 & Yes \\ \hline \end{tabular}
\end{table} 
Table 5: Non-parametric test using testing dataset. followed by GANFIS, the FAANFIS and the ANN (Figures 5-8 and Tables 3-5). It is because the GANFIS, PSOANFIS, FAANFIS used the GA, PSO, FA to optimize parameters, thereby reducing the error of prediction. The calculation of these methods is very simple, occupies the bigger optimization ability and can be completed easily. Although ML methods such as PSOANFIS, GANFIS, FAANFIS, ANN has a great potentiality for prediction problems, their predictive capability depends substantial on the quality of dataset. In term of prediction problems, the variables used have determination from various experiments on various sample can have effect on the predictive capability of the models. To improve the capability of the ML models, one of many solutions is that it needs to be provided more numbers of data. In this study, four potential ML techniques have low average error rates, so that it might be used to predict other properties of the road surface.