## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **R\({}^{2}\)** & **MAE** & **MSE** & **RMS** & **Duration** \\ \hline \multicolumn{6}{c}{Binary Output for Magnitude} \\ \hline \multicolumn{6}{c}{Time to train: 600 s} \\ \hline FastForestRegression & 0.9962 & 0.00 & 0.00 & 0.01 & 0.2 \\ FastTreeRegression & 0.9990 & 0.00 & 0.00 & 0.00 & 0.2 \\ FastTreeTweedieRegression & 0.8298 & 0.00 & 0.00 & 0.05 & 0.3 \\ LbfgsPoissonRegressionRegression & 0.0297 & 0.04 & 0.02 & 0.13 & 0.1 \\ LightGbmRegression & 0.9993 & 0.00 & 0.00 & 0.00 & 0.1 \\ SdcaRegression & 0.0132 & 0.04 & 0.02 & 0.13 & 0.1 \\ \hline \multicolumn{6}{c}{Time to train: 6000 s} \\ \hline FastForestRegression & 0.9874 & 0.01 & 0.00 & 0.02 & 0.02 \\ FastTreeRegression & 0.9951 & 0.00 & 0.00 & 0.01 & 38.1 \\ FastTreeTweedieRegression & 0.9999 & 0.00 & 0.00 & 0.00 & 0.3 \\ LbfgsPoissonRegressionRegression & 0.0764 & 0.05 & 0.03 & 0.17 & 0.1 \\ LightGbmRegression & 0.9995 & 0.00 & 0.00 & 0.00 & 0.1 \\ SdcaRegression & 0.0208 & 0.06 & 0.03 & 0.17 & 0.1 \\ \hline \multicolumn{6}{c}{Numerical Output for Magnitude} \\ \hline \multicolumn{6}{c}{Time to train: 600 s} \\ \hline FastForestRegression & 0.8942 & 0.02 & 0.03 & 0.18 & 0.3 \\ FastTreeRegression & 0.9006 & 0.02 & 0.03 & 0.17 & 0.3 \\ FastTreeTreewideRegression & 0.8532 & 0.02 & 0.04 & 0.21 & 0.3 \\ LbfgsPoissonRegressionRegression & 0.4554 & 0.11 & 0.16 & 0.41 & 0.1 \\ LightGbmRegression & 0.8360 & 0.03 & 0.05 & 0.22 & 0.1 \\ SdcaRegression & 0.0132 & 0.16 & 0.30 & 0.55 & 0.2 \\ \hline \multicolumn{6}{c}{Time to train: 6000 s} \\ \hline FastForestRegression & 0.9476 & 0.02 & 0.02 & 0.15 & 0.7 \\ FastTreeRegression & 0.9534 & 0.03 & 0.02 & 0.14 & 0.9 \\ FastTreeTweedieRegression & 0.8163 & 0.04 & 0.08 & 0.28 & 0.8 \\ LbfgsPoissonRegressionRegression & 0.5749 & 0.12 & 0.18 & 0.43 & 0.3 \\ LightGbmRegression & 0.9513 & 0.02 & 0.02 & 0.14 & 0.2 \\ SdcaRegression & 0.0232 & 0.19 & 0.42 & 0.65 & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of analyzed algorithms by ML_NET using different training times. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Type** & **R\({}^{2}\)** & **MAE** & **MSE** & **RMS** \\ \hline All Parameters & 0.9999 & 0.00 & 0.00 & 0.01 \\ Without Temperature & 0.8904 & 0.00 & 0.00 & 0.06 \\ Without Pressure & \(-0.3578\) & 0.04 & 0.04 & 0.22 \\ Without Precipitation & 0.8398 & 0.00 & 0.00 & 0.07 \\ Without Humidity & 0.8946 & 0.04 & 0.02 & 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation Study Results Comparison. remaining variables and the prediction output) and an MAE of 0.0492. These results suggest that pressure influences the model performance, and its absence reduces prediction accuracy;
* Without Precipitation. When precipitation was removed, R2 decreased to 0.8398, and MAE increased to 0.0059. Although the impact is less severe than removing pressure, the results indicate that precipitation is an essential parameter in prediction performance;
* Without Humidity. Removing humidity had a moderate effect, with an R2 of 0.8946, an MAE of 0.0046, and an RMS of 0.0617. These values suggest humidity is less influential than pressure and temperature but contributes positively to the model's performance. The ablation study results demonstrate that pressure and temperature are the most important components for prediction accuracy, with their removal having a negative impact. Humidity and precipitation also contribute to the EMM's performance but to a lesser degree. This analysis supports the inclusion of each of these parameters in the model and highlights the model's strong dependence on pressure and temperature variables for accurate seismic event prediction. To assess the computational efficiency of the EMM, we implemented the following methodology:  
* Time Complexity Measurement using the Stopwatch class in C# to measure the execution time of the process. This allowed for a time capture for critical operations within the training pipeline. The total execution time was 790 ms.