# Predicting Models for Local Sedimentary Basin Effect Using a Convolutional Neural Network
## 5 Basin Amplification Characteristics Prediction
### Optimize Input Parameters to Build Prediction Models
#### 5.3.4 Comparison of Results of Prediction Models with Different Input Parameters  
Based on the four factors \(S\), \(C\), \(\theta_{1}\), and \(\theta_{2}\), the CNN-AP-4 prediction model was first established. As shown in Figure 20a, we found that the recognition ability of the neural network was poor due to the limited input parameters. On this basis, the relative importance of \(H\),\(f\), and damping coefficients \(\alpha\) and \(\beta\) were studied, respectively. As shown in Table 11, six prediction models, CNN-AP-5 (\(H\)), CNN-AP-5 (\(f\)), CNN-AP-6 (\(\alpha\beta\)), CNN-AP-6 (\(Hf\)), CNN-AP-7 (\(\alpha\beta H\)), and CNN-AP-7 (\(\alpha\beta f\)), were established to discuss and analyze the influence of each factor on the prediction model. The prediction results of each model for the amplification factor are shown in Figure 20b and Table 12, and the prediction results of the hazard location are shown in Table 13.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**No.** & \(S\) & \(C\) & \(H\)/**m** & \(\theta_{1}\)/\({}^{c}\) & \(\theta_{2}\)/\({}^{c}\) & \(\alpha\) & \(\beta\) & \(f\)/Hz \\ \hline
1 & 0.2 & 2.5 & 300 & 15 & 15 & 0.044 & 0.00088 & 0.5 \\
2 & 0.2 & 2.5 & 300 & 20 & 20 & 0.044 & 0.00088 & 0.5 \\
3 & 0.2 & 2.5 & 300 & 30 & 30 & 0.044 & 0.00088 & 0.5 \\
4 & 0.2 & 2.5 & 300 & 45 & 45 & 0.044 & 0.00088 & 0.5 \\
5 & 0.2 & 2.5 & 300 & 60 & 60 & 0.044 & 0.00088 & 0.5 \\
6 & 0.2 & 2.5 & 300 & 80 & 80 & 0.044 & 0.00088 & 0.5 \\
7 & 0.2 & 2.5 & 300 & 20 & 30 & 0.044 & 0.00088 & 0.5 \\
8 & 0.2 & 2.5 & 300 & 20 & 60 & 0.044 & 0.00088 & 0.5 \\ \hline \end{tabular}
\end{table}
Table 10: Different angles parameters of basin.  
Figure 19: Max value of amplification factor and hazard location from different angles. (**a**) Amplification factor. (**b**) Hazard location.  
It can be seen from the results that CNN-AP-6 (_Hf_) is the best in terms of amplification coefficient prediction, followed by CNN-AP-5 (_f_), whose indexes are better than CNN-AP-8. CNN-AP-6 (_Hf_) is the best in the prediction of hazard location, followed by CNN-AP-5 (_f_), whose indexes are better than CNN-AP-8. In addition, CNN-AP-4 and CNN-AP-6 (\(\alpha\beta\)) are worse than CNN-AP-8 in terms of amplification factor and hazard location prediction. A negative \(R^{2}\) indicates that the performance of the regression model is worse than the average. By comparing the CNN-AP-4, CNN-AP-5 (_f_), and CNN-AP-5 (_H_) models, it can be found that, based on the CNN-AP-4 model, the accuracy of predicting amplification factor  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Input Layer Parameter** & **Output Layer Parameter** \\ \hline CNN-AP-5 (_H_) & \(S\), \(C\), \(\theta_{1}\), \(\theta_{2}\), \(H\) & \\ CNN-AP-5 (_f_) & \(S\), \(C\), \(\theta_{1}\), \(\theta_{2}\), \(f\) & \\ CNN-AP-6 (\(\alpha\beta\)) & \(S\), \(C\), \(\theta_{1}\), \(\theta_{2}\), \(\alpha\), \(\beta\) & \\ CNN-AP-6 (_Hf_) & \(S\), \(C\), \(\theta_{1}\), \(\theta_{2}\), \(H\), \(f\) & \\ CNN-AP-7 (\(\alpha\beta\)_Hf_) & \(S\), \(C\), \(\theta_{1}\), \(\theta_{2}\), \(\alpha\), \(\beta\), \(H\) & \\ CNN-AP-7 (\(\alpha\beta\)_Hf_) & \(S\), \(C\), \(\theta_{1}\), \(\theta_{2}\), \(\alpha\), \(\beta\), \(f\) & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Input and output parameters of prediction models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & _MAE_ & _RMSE_ & \(\mathbf{R^{2}}\) \\ \hline CNN-AP-8 & 0.0421 & 0.0575 & 0.9853 \\ CNN-AP-4 & 0.5338 & 0.6892 & \(-1.0192\) \\ CNN-AP-5 (_H_) & 0.0798 & 0.0946 & 0.9619 \\ CNN-AP-5 (_f_) & 0.0327 & 0.0469 & 0.9906 \\ CNN  
and hazard location has been greatly improved after adding fundamental frequency or soil thickness parameters. Meanwhile, the same rule can be found by comparing CNN-AP-6 (\(\alpha\beta\)), CNN-AP-7 (\(\alpha\beta\)H), and CNN-AP-7 (\(\alpha\beta\)f) models.