## 4 Results and Discussion
### Synthetic Dataset  
Table 5 demonstrates a statistical description of the synthetic compared to original data. The mean values of the features in the synthesized data closely approximated those of the original dataset, indicating that the TGAN model effectively captured the central tendencies inherent in the data. Additionally, the standard deviations exuded by the generalized data were compatible with those of the original data, subsuming that the TGAN model successfully replicated the disposition or variability present in the dataset. The examination of the minimum and maximum values of each feature further revealed that the range spread by the synthetic data aligned well with that of the original data, ensuring that the synthetic data does not contain any unrealistic outliers or values outside the valid range. Furthermore, skewness, a measure of asymmetry in the distributions, was highly similar between the two datasets, indicating that the TGAN model retained the shape characteristics of the original data distributions. The fact that the mean, standard deviation, minimum, maximum, and skewness values of the features in the synthetic data are effectively the same as those of the original data suggests that the generated data points capture the key statistical properties and patterns present in the real dataset. Figure 6 presents a t-SNE plot comparing the original data and the synthetic data generated by the TGAN model. The t-SNE algorithm is a dimensionality reduction technique that projects high-dimensional data into a lower-dimensional space (in this case, a \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Model** & **Dataset** & **R\({}^{2}\)** & **MEA** & **RMSE** & **MAPE** & **RSR** & **WMAPE** \\ \hline LightGBM & Train & 0.819 & 4.357 & 6.287 & 17.50\% & 0.409 & 11.06\% \\  & Test & 0.805 & 5.939 & 7.24 & 20.10\% & 0.600 & 14.921\% \\ \hline SVM & Train & 0.893 & 3.945 & 5.277 & 26.00\% & 0.333 & 11.276\% \\  & Test & 0.783 & 4.918 & 7.309 & 22.00\% & 0.496 & 13.749\% \\ \hline CFNN & Train & 0.902 & 2.973 & 3.348 & 6.00\% & 0.227 & 3.444\% \\  & Test & 0.842 & 3.940 & 6.442 & 18.00\% & 0.411 & 10.758\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model performance before data augmentation. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Symbol**} & \multirow{2}{*}{**Unit**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**STD**} & \multicolumn{2}{c}{**Min**} & \multicolumn{2}{c}{**Max**} & \multicolumn{2}{c}{**Skewness**} \\ \cline{3-10}  & & **Original** & **Synthetic** & **Original** & **Synthetic** & **Original** & **Synthetic** & **Original** & **Synthetic** \\ \hline FA & kg/m\({}^{3}\) & 246.72 & 238.01 & 159.99 & 150.28 & 0.00 & 0.00 & 620.00 & 546.91 & \(-\)0.34 & \(-\)0.20 \\ GGBS & kg/m\({}^{3}\) & 176.27 & 188.92 & 177.38 & 172.51 & 0.00 & 0.00 & 560.00 & 560.00 & 0.48 & 0.40 \\ SS & kg/m\({}^{3}\) & 117.56 & 119.44 & 36.08 & 36.01 & 32.00 & 33.11 & 205.50 & 203.66 & 0.23 & 0.23 \\ SH & kg/m\({}^{3}\) & 44.78 & 48.60 & 22.51 & 23.93 & 0.00 & 3.26 & 108.00 & 108.00 & 0.12 & 0.09 \\ SP & kg/m\({}^{3}\) & 1.52 & 1.69 & 2.57 & 2.54 & 0.00 & 0.00 & 9.00 & 8.33 & 1.35 & 1.13 \\ fng & kg/m\({}^{3}\) & 656.26 & 656.37 & 81.65 & 80.66 & 495.00 & 496.07 & 887.00 & 859.69 & 0.31 & 0.18 \\ cag & kg/m\({}^{3}\) & 1082.85 & 106.82 & 229.87 & 246.23 & 525.40 & 528.40 & 138.35 & 135.08 & \(-\)1.31 & \(-\)1.08 \\ w & kg/m\({}^{3}\) & 64.72 & 70.93 & 62.10 & 60.39 & 0.00 & 0.00 & 216.00 & 212.41 & 0.47 & 0.29 \\ CT & \({}^{<}\)C & 48.16 & 49.90 & 28.21 & 28.09 & 19.00 & 19.00 & 100 & 100.00 & 0.50 & 0.41 \\ CP & days & 21.76 & 22.54 & 14.99 & 14.44 & 1.00 & 1.00 & 56 & 56.00 & 0.62 & 0.63 \\ CS & MPa & 38.45 & 35.05 & 14.95 & 16.48 & 1.25 & 0.00 & 80.51 & 80.51 & 0.13 & 0.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of original and synthetic datasets. 2D plane) while preserving the local structure and relationships between data points [11].