## 7 Conclusions  
In this study, SVR was used to model the relationship between discrete MSI and continuous ground motion parameters. MSI is treated as sample target, and the 16 ground motion parameters are considered as feature candidates. In the Sichuan-Yunnan region, 125 sets of ground motion records with corresponding investigated MSIs were used as a complete dataset for analysis. Based on the limited dataset, the main conclusions are as follows:  
(1) During the single-feature scanning test, PGA, JMA equivalent acceleration, acceleration spectrum intensity, Housner intensity, PGV, velocity spectrum intensity, Arias intensity, characteristic intensity, and damage index are the most relevant features. Unlike the linear regression method, PGA is better than PGV for predicting MSI in an SVR model.  
(2) The best model parameters for Gaussian kernel SVRs with one all the way up to nine features are provided. The SVR of PGA gives almost the same performance as that of SVR with nine features. According to the performance measures of MSE, the correlation coefficient, and accuracy percentage, the Gaussian kernel SVRs perform much better than the liner kernels and linear regressions.  
(3) Gaussian kernel SVRs perform much better than previous models [3; 5; 7], especially with regard to the accuracy percentage. The comparison results also suggest that regression should better be done with a regional dataset.  
(4) Gaussian kernel SVRs with or without earthquake magnitude and epicentral distance give similar prediction performance.  
Since MSI and ground motion parameters have strong regional dependence, and the number of datasets for establishing the relationship in the studied area is limited, the conclusions may not be true  
Figure 10: Performance comparison of the SVR model with and without earthquake magnitude and epicentral distance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Performance**} & \multicolumn{2}{c}{**Reference [3] Model**} & \multicolumn{2}{c}{**Reference [5] Model**} & \multicolumn{2}{c}{**Reference [7] Model**} & \multicolumn{1}{c}{**SVR**} \\  & **PGA** & **PGV** & **PGA** & **PGV** & **PGA** & **PGV** & **PGA** \\ \hline MSE & 1.479 & 2.024 & 0.986 & 0.590 & 1.162 & 1.096 & 0.300 \\ Correlation coefficient & 0.704 & 0.705 & 0.708 & 0.691 & 0.656 & 0.676 & 0.768 \\ Accuracy percentage & 32.0\% & 13.6\% & 42.4\% & 47.2\% & 47.2\% & 35.2\% & 71.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance measures for nonlinear SVR and linear regressions based on PGA and PGV.  
anymore when another dataset is used for regression. However, a Gaussian kernel SVR of PGA is a good initial start for the regression.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/9/3086/s1](http://www.mdpi.com/2076-3417/10/9/3086/s1)]([http://www.mdpi.com/2076-3417/10/9/3086/s1](http://www.mdpi.com/2076-3417/10/9/3086/s1)). Supplement Table S1.  
Conceptualization, S.L. ([PERSON]) and Q.M.; methodology, D.T. and D.L.; software, D.T. and S.L. ([PERSON]); writing--original draft preparation, D.T. and Z.X.; writing--review and editing, D.T.; project administration, D.T.; funding acquisition, Q.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration (Grant Nos. 2014B08, 2016A03), National Key Research and Development Program of China (Grant No. 2017 YFC1500802), and Shandong Co-Innovation Center for Disaster Prevention and Mitigation of Civil Structures (Grant No. XTZ201901).  
We thank the China Strong Motion Networks Center, Institute of Engineering Mechanics, China Earthquake Administration for providing the ground motion records and macroseismic intensity maps. We also thank [PERSON] and [PERSON] for providing the Generic Mapping Tools software.  
The authors declare no conflict of interest.