# A Prediction Model of Marine Geomagnetic Diurnal Variation Using Machine Learning
## 5 Experimental Result Analysis
### Example Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**BEL**} & \multicolumn{3}{c}{**SVM-RF**} & \multirow{2}{*}{**Distance**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**Two-Factor**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{5-6}  & **Combination** & & & & & & \\ \cline{2-2} \cline{5-6}  & **Weight** & & & & & & \\ \hline RMSE/nT & 1.2126 & & 2.2361 & 2.1592 & 2.2181 & 45\% \\ MAE/nT & 0.8852 & & 1.6365 & 1.6458 & 1.6699 & 46\% \\ Correlation & 0.90 & & 0.67 & 0.68 & 0.68 & 34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison and analysis table of diurnal geomagnetic variation calculation results at the BEL station. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**HALP**} & \multicolumn{3}{c}{**SVM-RF**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**Two-Factor**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{5-6}  & **Combination** & & & & & \\ \cline{2-2} \cline{5-6}  & **Weight** & & & & & \\ \cline{2-2} \cline{5-6}  & **Weight** & & & & & \\ \hline RMSE/nT & 1.0530 & & 1.2160 & 2.1599 & 1.7967 & 39\% \\ MAE/nT & 0.7701 & & 0.9070 & 1.7133 & 1.4119 & 48\% \\ Correlation & 0.91 & & 0.82 & 0.73 & 0.81 & 16\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison and analysis table of diurnal geomagnetic variation calculation results at the HLP station. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**HALP**} & \multicolumn{3}{c}{**SVM-RF**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**Two-Factor**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{5-6}  & **Combination** & & & & & \\ \cline{2-2} \cline{5-6}  & **Weight** & & & & & \\ \hline RMSE/nT & 2.0712 & & 2.0340 & 2.0860 & 2.0806 & 26\% \\ Correlation & 0.80 & & 0.68 & 0.63 & 0.64 & 23\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison and analysis table of diurnal geomagnetic variation calculation results at the HAD station. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**FUR**} & \multicolumn{3}{c}{**SVM-RF**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**Two-Factor**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{5-6}  & **Combination** & & & & & \\ \cline{2-2} \cline{5-6}  & **Weight** & & & & & \\ \hline RMSE/nT & 1.9345 & & 2.2683 & 2.0504 & 2.0554 & 9\% \\ MAE/nT & 1.5188 & & 1.91 & 1.641 & 1.6303 & 13\% \\ Correlation & 0.80 & & 0.77 & 0.78 & 0.78 & 3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison and analysis table of diurnal geomagnetic variation calculation results at the FUR station. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**FUR**} & \multicolumn{3}{c}{**SVM-RF**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**Two-Factor**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{5-6}  & **Combination** & & & & & \\ \cline{2-2} \cline{5-6}  & **Weight** & & & & & \\ \hline RMSE/nT & 1.9345 & & 2.2683 & 2.0504 & 2.0554 & 9\% \\ MAE/nT & 1.5188 & & 1.91 & 1.641 & 1.6303 & 13\% \\ Correlation & 0.80 & & 0.77 & 0.78 & 0.78 & 3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison and analysis table of diurnal geomagnetic variation calculation results at the ESK station. The results highlight the superior performance of the SVM-RF fusion model compared to the other three algorithms, as evidenced by reductions in the root mean square error (RMSE) and mean absolute error (MAE). The RMSE decreased by 39%, 27%, 9%, 35%, and 45% under different station selection schemes, while the MAE decreased by 48%, 26%, 13%, 41%, and 46%, correspondingly. Furthermore, the Pearson correlation coefficients saw notable increases of 16%, 23%, 3%, 24%, and 34%, respectively.