## 3 Results and Discussion
### Prediction Results of CNN-PSPA Models
#### 3.3.3 Comparison with CNN-FSPA Model  
To estimate the ability of CNN-PSPA prediction models, all the six trained models (non-SOCS, non-PGMS, non-TAIS, non-FAIS, non-BMTS and non-LHES) were used to predict the site amplification factors at the corresponding stations. For example, the trained non-SOCS model predicted the site amplification factor at the SOCS station. The test-sample data were selected using the four earthquakes that were also used to test the CNN-FSPA model. All error values for the site amplification factors at the six soil stations were calculated from the six trained CNN-PSPA models. The corresponding errors for the same four earthquakes at the six station locations based on the trained CNN-FSPA models were also predicted for comparisons. All the errors of the predicted values for these seven CNN models are listed in Table 12.  
Table 12 shows that since the prediction errors of the CNN-FSPA model were lower than those of the CNN-PSPA model, the CNN-FSPA model was significantly better than the CNN-PSPA model. This phenomenon shows that the more station data that are included in the CNN training, the better the CNN model is trained. The included test information could influence the accuracy and credibility of the prediction results. Table 12 also shows that although the trained CNN-PSPA model had higher errors than the trained CNN-FSPA model in any prediction, it had relatively smaller differences, demonstrating that reliable amplification factors could be obtained even using the trained CNN-PSPA model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Magnitude**} & \multirow{2}{*}{**Predicted**} & \multicolumn{2}{c}{**CNN-PSPA**} & \multicolumn{2}{c}{**CNN-FSPA**} \\ \cline{3-5}  & & **Models** & **Error** & **Error** \\ \hline \multirow{6}{*}{M4.5} & SOCS & non-SOCS & 21.7\% & 4.2\% \\  & PGMS & non-PGMS & 29.9\% & 12.9\% \\  & TAIS & non-TAIS & 37.9\% & 13.5\% \\  & FAIS & non-FAIS & 16.8\% & 19.6\% \\  & BMTS & non-BMTS & 12.1\% & 26.9\% \\  & LHES & non-LHES & 16.5\% & 8.1\% \\ \hline \multirow{6}{*}{M5.6} & SOCS & non-SOCS & 16.6\% & 1.3\% \\  & PGMS & non-PGMS & 21.7\% & 4.4\% \\ \cline{1-1}  & TAIS & non-TAIS & 1.8\% & 1.6\% \\ \cline{1-1}  & FAIS & non-FAIS & 4.7\% & 2.6\% \\ \cline{1-1}  & BMTS & non-BMTS & 21.0\% & 5.8\% \\ \cline{1-1}  & LHES & non-LHES & 5.5\% & 5.1\% \\ \hline \multirow{6}{*}{M5.1} & SOCS & non-SOCS & 10.2\% & 2.5\% \\  & PGMS & non-PGMS & 16.5\% & 1.9\% \\ \cline{1-1}  & TAIS & non-TAIS & 13.5\% & 11.5\% \\ \cline{1-1}  & FAIS & non-FAIS & 1.7\% & 1.5\% \\ \cline{1-1}  & BMTS & non-BMTS & 20.8\% & 12.4\% \\ \cline{1-1}  & LHES & non-LHES & 10.4\% & 4.1\% \\ \hline \multirow{6}{*}{M4.0} & SOCS & non-SOCS & 10.0\% & 21.4\% \\ \cline{1-1}  & PGMS & non-PGMS & 21.3\% & 16.5\% \\ \cline{1-1}  & TAIS & non-TAIS & 12.7\% & 20.5\% \\ \cline{1-1}  & FAIS & non-FAIS & 17.3\% & 3.7\% \\ \cline{1-1}  & BMTS & non-BMTS & 12.2\% & 3.7\% \\ \cline{1-1}  & LHES & non-LHES & 26.5\% & 22.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Errors for CNN-FSPA and CNN-PSPA.  
Table 12 also shows that, as for the predictive regularities of the CNN-FSPA models, the CNN-PSPA predictions for the medium-sized earthquakes such as M5.1 and M5.6 were better than those for micro-earthquakes such as M4.0 and M4.5. The non-FAIS model showed outstanding predictive power for medium earthquakes, even though it exhibited a relatively high error for the micro-earthquakes such as M4.0 and M4.5. In contrast, for the CNN-FSPA model, it should be noted that the non-FAIS and non-BMTS models for M4.5 and the non-TAIS model for M4.0 unexpectedly exhibited higher error rates than the CNN-PSPA model.