## 3 Results
### Comparison Between Machine Learning Techniques and Traditional Methods
#### 3.4.2 Considerations  
While ML and DL models offer advantages compared to traditional methods, their widespread adoption in EEW systems requires further validation across diverse seismic networks. Real-time deployment introduces additional constraints, including the need for low-latency inference and robust performance under varying seismic conditions. Challenges such as data imbalance, limited availability of large-magnitude event records, and computational demands for real-time deployment must be addressed to ensure reliable performance in operational settings [34; 56]. Beyond computational limitations, several key factors explain the continued preference for traditional methods in operational seismic warning systems:  
* Interpretability and trustworthiness: Traditional methods like \(\tau_{c}\) and \(P_{d}\) are based on well-established physical principles, making them transparent and easy to validate. In contrast, ML/DL models function as \"black boxes\", where the decision-making process is not easily interpretable. However, recent advancements in explainable AI (XAI) [69] have introduced techniques to enhance interpretability, such as Grad-CAM [70], SHapley Additive Explanations (SHAP) [71], and local interpretable model-agnostic explanations (LIME) [72]. Although these techniques have been applied in various fields, such as healthcare [73], finance [74], and image recognition [75], their potential in seismology remains largely unexplored. Further research is needed to assess their effectiveness in improving the transparency of ML/DL models for seismic applications, ensuring that these methods can be reliably integrated into real-time earthquake early warning systems. * Generalization and adaptability across different seismic regions: Traditional empirical models have well-defined calibration parameters that can be adapted with minimal regional data, whereas ML/DL models require large datasets to prevent overfitting. The variability in tectonic conditions across different regions can significantly impact the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **MAE (Magnitude Units)** & **Standard Deviation (\(\sigma\))** \\ \hline \(\tau_{c}\)[9] & 1.637 & 1.45 \\ \(P_{d}\)[10] & 0.425 & 0.41 \\ SVM-M [40] & 0.297 & 0.31 \\ CNN-LSTM [32] & 0.25 & 0.25 \\ Transformer [33] & 0.15 & 0.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison between traditional and ML-/DL-based magnitude estimation methods. performance of ML/DL approaches [4, 68]. To improve model adaptability, researchers have explored techniques such as fine-tuning [76] and domain adaptation [77]. * Robustness in data-limited or noisy conditions: In regions with low-density seismic networks, traditional methods can still function with minimal data, whereas ML/DL models require large-scale datasets for training and may fail when encountering unseen or noisy data. To address these limitations, researchers have explored techniques such as data augmentation [78] and self-supervised learning [79]. * Regulatory and approval constraints in EEW systems:The deployment of new ML/DL models in operational seismic warning systems requires extensive validation, certification, and regulatory approval. Traditional methods have been used and optimized for decades, whereas ML-based approaches must undergo a rigorous evaluation before being integrated into national or regional networks [34, 56]. * Practical benefits and real-world impact: Although ML-based models have demonstrated superior accuracy in magnitude estimation, their actual contribution to improving EEW performance and reducing earthquake-related losses remains an open research topic. Some studies suggest that ML models can reduce false alarms and missed detections, thereby improving public trust in EEW systems [4, 7]. However, large-scale evaluations and real-world implementation studies are still needed to quantify their effectiveness in reducing casualties and economic damage.