## 4 Discussion  
### Comparison of Single Type Data and Combined Data in the CUI Rate Model  
In determining the best model for data fitness, the comparison of single data type and combined data type was done. For single data type, the individual experimental and field result was modeled as output separately. Then, the combined data from experimental and field were model and compared, as in Figure 7.  
Based on the comparison model in Figure 7, the experimental results demonstrate much lower corrosion rate values by time, especially after 20 years of time elapsed, which is shown in the yield curve. This prediction might fall into the unhealthy prediction rate for CUI and be dangerous if real piping operation follows this model. Thus, it can be emphasized that the short-term experimental test alone is not reliable for the development of the overall prediction model even though the range of neurons is enough for longer-term prediction as the model predicts lower corrosion loss than actual. However, the importance of this experimental work results is on the initiation part of the corrosion rate model. It shows a more accurate initiation of CUI rate development. Thus, this complements \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Nodes**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Testing**} \\ \cline{2-7}  & \(\mathbf{R^{2}}\) & **RMSE** & \(\mathbf{R^{2}}\) & **RMSE** & \(\mathbf{R^{2}}\) & **RMSE** \\ \hline
1 & 0.84 & 0.024 & 0.87 & 0.024 & 0.85 & 0.024 \\
2 & 0.85 & 0.018 & 0.89 & 0.018 & 0.86 & 0.018 \\
3 & 0.85 & 0.016 & 0.88 & 0.016 & 0.87 & 0.016 \\
4 & 0.86 & 0.015 & 0.89 & 0.015 & 0.87 & 0.015 \\
5 & 0.87 & 0.014 & 0.88 & 0.015 & 0.88 & 0.013 \\
6 & 0.88 & 0.012 & 0.89 & 0.014 & 0.87 & 0.014 \\
7 & 0.87 & 0.011 & 0.88 & 0.012 & 0.89 & 0.009 \\
8 & 0.99 & 0.009 & 0.90 & 0.010 & 0.91 & 0.009 \\
9 & 0.88 & 0.009 & 0.87 & 0.010 & 0.89 & 0.010 \\
10 & 0.89 & 0.008 & 0.87 & 0.011 & 0.86 & 0.011 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of neuron number in the hidden layer. Figure 6: Actual vs. predicted corrosion rate in millimeters per year for a neural network. (**a**) Training, (**b**) Validation, and (**c**) Testing. the model made by the field data alone. The model cannot be used to predict the initiation of CUI as opposed to the experimental work. This phenomenon can be understood clearly in Figure 7.  
The line model produced by the field data gave high corrosion loss values in the initial phase, although, in actual performance, CUI will not give this corrosion loss as its protective coating and insulation systems are supposedly still active. For this reason, the experimental data were used to complement the model. Despite the roles individually played by the actual field data and experimental data, both are very important. Thus, by integrating and combining both sources of data, the CUI prediction model can be the distinguishing factor in industries since most existing CUI models are dependent on either field or lab work separately. The comparison of the model developed based on single type data, either field or lab, and integration of multi-type data sources was clear. The smooth line fitting by the combined data sources in blue line, as shown in Figure 7, has a similar trend when associated with the model compiled by [PERSON] [10], and then followed the pitting corrosion model trend developed by [PERSON] for pitting corrosion except for the length of the years elapsed [25]. Even though [PERSON] [26] did not specify which type of corrosion will occur based on the timeframe, these main types of corrosion due to CUI are still tallied as recorded. For instance, the CUI prediction model consists of the specific elapsed time indicating the association for different phases; this might be considered as an improvement to the existing CUI models available worldwide, which do not embrace the deviations in corrosion mechanism with time.