## 6 Results and Discussion
### Gaussian Kernel SVR of PGA Versus Models from Previous Studies  
The final Gaussian kernel SVR of PGA is obtained by training the whole available dataset, and it can be used for predicting MSIs in future earthquakes. The final SVR model was compared with regression models from previous studies to check regression performance. Models from three previous studies [3; 5; 7] are compared here. These three models have regression equations based on both PGA and PGV. The results are shown in Figure 9a,b, and the performance measures are shown in Table 3. It is clear from Figure 9 that the performance of the SVR model is much better than that of the other three models, especially at MSI VI and VII. The predicted points of MSI VI and VII are well concentrated in a much smaller range in the SVR model, while the points in the other three models have much larger scatter. It is interesting that all models have relatively good behavior at MSI IX. From Table 3, though the correlation coefficients of the four models are at the same level, the accuracy percentage of the SVR model is much higher than the other three. The reason for this is because these three models have too much prediction dispersion at MSI VI and VII. It should be noted that one study [3] was based on California data, and another [5] on global data. Regional variation and differences in datasets result in bad performance for the Sichuan-Yunnan earthquakes. As the regression equation was obtained for the same dataset as that in SVR model, the MSEs of the linear regression in Table 2 are much smaller than those in Table 3. Thus, for a specific region, one should be very careful using the regression model of another region. The accuracy percentage of this study is also better than the third other model [7], and there are two reasons. One is that this study contained datasets from other areas of western China besides the Sichuan-Yunnan region, and did not contain Jiuzhaigou earthquake records. The other is that the filtering process was different from this paper, which leads to the condition that the PGA and PGV are not exactly the same for the two datasets. It is suggested that to have comparable results of different regression methods, not only should the earthquake records be the same, but also the ground motion parameters after the filtering process, as much as possible.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Performance** & \begin{tabular}{c} **Linear Regression** \\ **of PGA** \\ \end{tabular} & \begin{tabular}{c} **Linear Regression** \\ **of PGV** \\ \end{tabular} & \
\begin{tabular}{c} **SVR of PGA** \\ **of PGV** \\ \end{tabular} &
\begin{tabular}{c} **SVR of PGA** \\ **and PGV** \\ \end{tabular} \\ \hline MSE & 0.452 & 0.374 & 0.214 & 0.284 & 0.227 \\ Correlation coefficient & 0.599 & 0.715 & 0.819 & 0.759 & 0.814 \\ Accuracy percentage & 44.3\% & 66.7\% & 74.1\% & 67.1\% & 68.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance measures for Gaussian kernel SVR and the linear regression of peak ground acceleration (PGA) and peak ground velocity (PGV).  
Figure 9: Performance comparison of the Gaussian kernel SVR with previous models. (**a**) Reference model based on PGA; (**b**) reference model based on PGV.