# Predicting Models for Local Sedimentary Basin Effect Using a Convolutional Neural Network
## 6 Conclusions  
In this paper, an artificial intelligence method of CNN was used to predict the amplification characteristics of the standard basin, focusing on the influence of the geometrical and soil mechanical parameters of the basin on the surface amplification in the case of a distant earthquake ignoring the effects of the source and the propagation path. The parametric modeling of the finite element software ABAQUS was implemented to build a batch model of the basin. According to the amplification characteristics of the basin, the special neural networks for peak amplification coefficients and hazard locations, as well as a joint prediction network for both, were established, respectively. The three types of networks were trained to use the influencing factors of the basin amplification characteristics as the input of CNN to predict the surface amplification characteristics of standard basins and combined with Bayesian optimization methods to select the best network parameters to improve the accuracy of the network. Finally, the sensitivity of specific geometric and media parameters to the effect of surface amplification was analyzed based on the final selected eight-parameter joint prediction model CNN-AP-8 predictions. The results of the computational analysis show that:  
1. In predicting the location of amplification hazards, labels were assigned using vectors. The basin was divided into three categories: symmetric, small dip asymmetry, and big dip asymmetry. The prediction accuracy of the hazard location reached 94.70%, and the mean square error for predicting specific locations was about 2, as the width of the basin was divided into several areas of equal length of 40 m in this paper, which means that the error between the predicted and true specific location was about 80 m.
2. Comparing the eight-parameter CNN-AP-8 prediction model against two dedicated prediction models for amplification characteristics demonstrated that the joint prediction model could predict basin amplification characteristics comprehensively, and the accuracy was basically the same as that of two dedicated prediction models. 3. In the parameter sensitivity analysis, by comparing the accuracy of the model under different input parameters, it was found that the shape ratio, wave velocity ratio, dip angle, soil thickness, and fundamental frequency were very important input factors. The prediction model based on these factors has improved the accuracy of the amplification factor and the hazard location. The prediction accuracy of the hazard location was up to 97.35%, and the mean square error for predicting specific locations was about 1. The true error in the 40 m segmented basins model was about 40 m.
4. The basin models in this paper were all single-layer standard simplified models, which were still different from the actual basins. The soil stratification can be further considered in the future. At the same time, the incident wave also has an important effect on surface amplification. After further considering the effects of the incident \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Direction** & _MAE_ & _RMSE_ & \(\mathbf{R^{2}}\) \\ \hline CNN-AP-8 & 0.9249 & 1.2109 & 1.9128 & 0.9538 \\ CNN-AP-4 & 0.4040 & 10.5675 & 11.8515 & \(-\)0.5094 \\ CNN-AP-5 (\(H\)) & 0.9470 & 1.0091 & 2.6531 & 0.9544 \\ CNN-AP-5 (\(f\)) & 0.9669 & 0.9729 & 1.6513 & 0.9661 \\ CNN-AP-6 (\(\alpha\beta\)) & 0.3841 & 10.2799 & 12.4476 & \(-\)0.5890 \\ CNN-AP-6 (\(H\)f) & 0.9735 & 1.0291 & 1.6377 & 0.9758 \\ CNN-AP-7 (\(\alpha\beta\)f) & 0.9669 & 1.3036 & 1.8822 & 0.9481 \\ CNN-AP-7 (\(\alpha\beta\)f) & 0.9735 & 1.6464 & 1.9354 & 0.9598 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of evaluation indicators between CNN-AP-8 model and different prediction models on hazard location. wave frequency, incident angle, and other factors, and whether the CNN can have a better prediction effect needs further study.