## 3 Results
### Early Earthquake Magnitude Estimation as a Regression Task
It predicts magnitudes using the first 3, 4, and 5 s of P-wave signals, extracting 34 features related to waveform characteristics and site-specific parameters. The ensemble integrates XGBoost, random forest, LightGBM, and CatBoost, optimized with the sanitized gray wolf optimizer (SGWO). Unlike conventional ML models, MagPred leverages stacked ensemble learning, combining multiple algorithms to enhance prediction accuracy and robustness. The model outperforms traditional approaches like \(\tau_{c}\) and \(P_{d}\), achieving an MAE of 0.41, 0.40, and 0.38 for 3, 4, and 5 s of P-wave data, respectively. Additionally, it demonstrates generalization capabilities by performing well on earthquakes from different regions, such as India and Nepal, suggesting its applicability beyond the original training dataset. While effective for moderate earthquakes (\(M_{s}\) 3.0-5.0), it underestimates larger events, likely due to the limited presence of high-magnitude records. This limitation highlights the need for improved high-magnitude data representation, possibly through more advanced augmentation techniques or hybrid training strategies combining real and synthetic data. A key contribution is the use of GAN-based synthetic data, which improves magnitude estimation for early warning applications. However, the reliance on synthetic data may introduce biases, particularly if real earthquake distributions differ significantly from generated samples, potentially affecting performance in extreme cases. Future work could integrate real-time adaptive learning techniques to enhance robustness. Finally, Ref. [34] introduced DFTQuake, a deep learning model designed for real-time early magnitude and peak ground acceleration (PGA) prediction, integrating tripartite Fourier transform attention and dendritic neural networks. The architecture incorporates a Fourier transform attention mechanism that captures seismic signal features in the frequency domain, enhancing interpretability and improving magnitude estimation accuracy. Unlike previous approaches, DFTQuake replaces traditional fully connected layers with a dendritic neural network, which introduces localized non-linearity, improving feature representation and reducing computational overhead. The model was trained using strong motion datasets from Japan, India, and other regions, demonstrating its ability to generalize across seismic environments. It processes the first three seconds of P-wave data and generates predictions with an MAE of 0.66 \(M_{JMA}\) for magnitude and 0.25 gal for PGA. Comparisons against existing models, including Vision Transformers (ViT) and LSTM-based networks, show that DFTQuake achieves a 13.16% improvement in magnitude estimation and a 34.21% improvement in PGA prediction. One key advantage of DFTQuake is its robustness to variations in epicentral distance, as it does not impose upper limits on distance ranges, unlike other deep learning models. However, despite its advantages, the model exhibits higher uncertainty for large-magnitude events due to the limited number of high-magnitude samples in training data. Future enhancements could involve integrating additional regional datasets and refining the dendritic neural network structure to further improve real-time prediction accuracy.