# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 6 Conclusions  
This study explores the application of machine learning models to predicting the maximum earthquake magnitude, focusing on the southern section of the North-South Seismic Belt in China. By analyzing and processing earthquake catalog data, 11 seismic activity indicators were extracted. A sliding time window method was used for modeling and prediction. After comparing six machine learning models, it was found that the LSTM model performed best in predicting the maximum magnitude for the next year. The conclusions are as follows:  
1. The LSTM model excels at capturing long-term dependencies in time series data, particularly in predicting earthquake events in the magnitude range of 5 < \(M_{\rm L}\)\(\leq\) 6, achieving a prediction success rate of 85%. Additionally, when the model is applied to a broader research area, it still performs well, verifying its universality. This finding indicates that, compared to other models, the LSTM model better interprets the complex nonlinear relationships between seismic activity parameters and future magnitudes, leading to higher prediction accuracy and robustness. 2. A longer time window (ten years) and an 8:2 ratio of training to testing data are crucial for improving the model's prediction accuracy. A longer time window ensures more seismic events are included, leading to more stable and reliable seismic parameters. The 8:2 training-to-testing ratio better balances model training and validation, resulting in more robust performance on the test set. 3. Among the 11 selected seismicity parameters, the \(b\)-value and the square root of total seismic energy have been critical in improving the model's prediction accuracy. The  
Figure 10: Confusion matrix of the LSTM model’s prediction results on the test set, with the true values on the horizontal axis and the predicted values on the vertical axis; yellow numbers are accurate predictions and blue data are inaccurate predictions. \(b\)-value reflects the frequency ratio of small to large earthquakes, while the square root of total seismic energy provides information about the energy of seismic events. These parameters comprehensively reflect the spatiotemporal intensity relationship of seismic activity and are crucial for magnitude prediction. * The magnitude interval-based assessment method proposed in this paper not only focuses on the difference between the predicted and actual magnitude but also reflects the actual impacts that earthquakes may bring. For example, accurate predictions of moderate to strong earthquakes are crucial for disaster prevention and mitigation, while predictions of lower magnitudes assist in designing early warning systems and the rational allocation of resources. Overall, the earthquake magnitude prediction model based on LSTM has good predictive ability and broad application prospects. Particularly in the seismically active southern section of the North-South Seismic Belt, the LSTM model can provide relatively accurate predictions for moderate to strong earthquakes. In the future, the model parameters and structure can be further optimized by incorporating the spatial and temporal distribution characteristics of seismic activity, as well as additional multi-source parameters (such as soil effects), to improve the accuracy and reliability of the model. This will provide strong support for the development of earthquake early warning, emergency response, and disaster mitigation measures. Conceptualization, J.Z. and K.S. ; methodology, J.Z. ; software, J.Z. and N.M.; validation, N.M. and K.S. ; formal analysis, J.Z. ; investigation, X.H. and N.M.; resources, J.Z. ; data curation, J.Z. and N.M.; writing--original draft preparation, J.Z. ; writing--review and editing, J.Z., K.S., X.H. and N.M.; visualization, J.Z. ; supervision, K.S. and X.H. ; project administration, K.S. ; funding acquisition, K.S. All authors have read and agreed to the published version of the manuscript.