## 5 Results
### Verification of the CPCI Prediction Model  
In this study, to verify the CPCI prediction model, the prediction reliability of the predicted data compared to actual data (30 years old and 27 years old) was analyzed while changing the number of particles for all data points in 206 and 261 sections. Table 3 presents the analysis results. As a result of the analysis, using 15 000 particles was the most efficient, as confirmed in Figure 5.  
Among the 467 sections included in this analysis, the data that comes in the 95% confidence interval for the 2.06 km section that predicts the age of 30 years is 204 out of the 208 sections, accounting for 98.1% of the total, leaving only 4 points (1.9%) counted as sections not included. The data that comes in the 95% confidence interval for the 2.61 km section that predicts the age of 27 years is 241 out of the 261 sections, accounting for 92.3% of the total, leaving 20 points (7.7%) counted as sections not included. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**206 Sections (in 30 Years)**} & \multicolumn{3}{c}{**261 Sections (in 27 Years)**} \\ \cline{2-7}
**Division** & **Detected** & **Predicted** & **Accuracy** & **Detected** & **Predicted** & **Accuracy** \\  & **CPCI** & **CPCI** & **(\%)** & **CPCI** & **CPCI** & **CPCI** & **(\%)** \\ \hline
4.2 (a),(b) & & 2.938 & 95.71 & & 3.195 & 94.21 \\ \hline
4.4 (c),(d) & 3.07 & 3.154 & 97.27 & 3.02 & 3.187 & 94.48 \\ \hline
4.6 (e),(f) & & 3.091 & 99.33 & & 3.121 & 96.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of prediction accuracy for each section according to changes in initial CPCI (4.2, 4.4, and 4.6). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**206 Sections (in 30 Years)**} & \multicolumn{3}{c}{**261 Sections (in 27 Years)**} \\ \cline{2-7}
**No. of** & \multicolumn{3}{c}{**95\% Confidential Interval**} \\ \cline{2-7}
**Particles** & **Included** & **Not** & **Validation** & **Accuracy** & **Included** & **Not** & **Validation** \\  & **Included** & **(\%)** & **(\%)** & **Included** & **Not** & **Accuracy** \\ \hline
1000 & 200 & 8 & 96.15 & 236 & 25 & 90.42 \\ \hline
5000 & 199 & 9 & 95.67 & 241 & 20 & 92.34 \\ \hline
10,000 & 202 & 6 & 97.12 & 241 & 20 & 92.34 \\ \hline
15,000 & 204 & 4 & 98.08 & 241 & 20 & 92.34 \\ \hline
20,000 & 201 & 7 & 96.63 & 241 & 20 & 92.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison and verification of prediction accuracy for each section according to the change in the number of particles. In this study, we analyzed the change in prediction accuracy by increasing the number of data points in the order of 3, 4, 5, and 6 (except for the initial CPCI) for the 2.06 km section with a total of 7 points. In Table 4, the results predicted through actual data are indicated in blue. Considering the analytic characteristics of particle filtering, the learning results and prediction accuracy of the actual data were at least 99.4%. Furthermore, as the length of the prediction period increased, the prediction accuracy gradually decreased. The relative error by the number of time-series points for the same section decreased as the number of points increased, as shown in Figure 6.