# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 5 Discussion
### Considering Different Parameters
This importance likely arises because the geographic location of significant earthquakes is often closely related to geological structures and fault activities, making mean longitude a vital spatial distribution parameter in the prediction model. In the study of Region B (Table 9), we observed different impacts of seismic activity parameters on the prediction model. The square root of total seismic energy is crucial in predicting earthquakes with magnitudes of \(5<M_{\rm L}\leq 6\). When this parameter is removed, the R-value drops from an overall 0.84 to 0.40, highlighting its importance in reflecting the energy release of earthquakes in the region. The b-value also plays a significant role in predicting earthquakes within this magnitude range. Its removal results in a noticeable decrease in the R-value. For predicting earthquakes greater than magnitude 6, the mean magnitude shows importance; its removal results in an R-value decrease from 0.64 to 0.25, indicating its role in capturing the overall intensity of seismic activity. However, the mean magnitude may introduce noise for predictions within the \(5<M_{\rm L}\leq 6\) range. When removed, the R-value increased to 1.00, suggesting that within a large region, the mean magnitude may not accurately represent the overall seismic activity due to the dispersion and diversity of earthquakes. The mean longitude contributes less to the large-region earthquake prediction, and there is no significant change in the R-value after deletion. Overall, the square root of total seismic energy and the b-value are crucial for enhancing prediction performance, while the mean magnitude's impact varies across different magnitude ranges and should be used cautiously to avoid introducing noise. Overall analysis indicates that: (1) The role of mean magnitude varies across different regions. In smaller regions, mean magnitude is crucial for predicting earthquakes with magnitudes of \(5<M_{\rm L}\leq 6\), but it may introduce noise in larger regions. (2) The significance of the b-value in earthquake prediction has been validated, with it showing an essential role across different regions and magnitude ranges, regardless of the calculation method. (3) The square root of total seismic energy and spatial parameters, like mean longitude and latitude, vary in importance depending on the region and magnitude range. While the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & \(5<M_{\rm L}\leq 6\) & \(M_{\rm L}>6\) \\ \hline \multirow{8}{*}{\(5<M_{\rm L}\leq 6\)} & None & 0.85 \\  & \(M_{\rm mean}\) & 0.55 \\  & \(E^{1/2}\) & 0.96 \\  & \(b_{1}\) & 0.82 \\  & \(a\) & 0.88 \\  & \(b_{2}\) & 0.69 \\  & \(\sigma\) & 0.64 \\  & \(\Delta M\) & 0.81 \\  & \(Lon_{mean}\) & 0.96 \\  & \(Lat_{mean}\) & 0.88 \\  & \(Lon_{E}\) & 0.81 \\  & \(Lat_{E}\) & 0.58 \\  & None & 0.37 \\  & \(M_{\rm mean}\) & 0.37 \\  & \(E^{1/2}\) & 0.37 \\  & \(b_{1}\) & 0.32 \\  & \(a\) & 0.37 \\ \(M_{\rm L}>6\) & \(b_{2}\) & 0.37 \\  & \(\sigma\) & 0.37 \\  & \(\Delta M\) & 0.37 \\  & \(Lon_{mean}\) & 0 \\  & \(Lat_{mean}\) & 0.37 \\  & \(Lon_{E}\) & 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Computed values of R scores for different predicted magnitudes, using different input vectors obtained by removing one parameter at a time for Region A.  
square root of total seismic energy might introduce noise in smaller regions, it is crucial for predicting earthquakes between 5 and 6 in larger regions. Spatial parameters, especially mean longitude, are vital for predicting large earthquakes in smaller regions but have a minor impact in larger regions, likely due to the dispersion of seismic activity over larger areas. (4) The model performs well in predicting earthquakes with a magnitude of 5 < \(M_{\rm L}\)\(\leq\) 6 but encounters challenges in predicting earthquakes with a magnitude of \(M_{\rm L}\) > 6.