## 4 Results
### Forecast Results  
#### 4.2.1 Strong Earthquake Occurrence and Classification  
Figure 6 illustrates the evaluation results of four traditional machine learning models (random forest (RF), decision tree (DT), support vector machine (SVM), and logistic regression (LR)) for the classification of large earthquake occurrence, namely, accuracy, precision, recall, and F1 score. The four models were implemented by calling the respective functions of SVM in the sklearn toolkit, LogisticRegression, tree in the ensemble, and RandomForestClassifier in the ensemble. For SVM, the kernel function was set to rbf, and the penalty relaxation variable was set to 1.0, with other parameters adopting default values. Pandas and NumPy were called here to read the dataset file and generate the corresponding array, respectively. Finally, matplotlib.pyplot was used to visualize the predicted results. The four evaluation indicators were calculated using Formulas (7)-(10). All tasks were completed using Python 3.9.6.  
The vertical axis of each subgraph in Figure 6 represents the respective evaluation metric value, while the horizontal axis represents the magnitude threshold based on the magnitude range of the catalog. For the experiment's training and test sets, the entire dataset was divided using a 7:3 ratio by calling the function train_test_split in the model_selection of the sklearn package.  
During the experiment, it was observed that using the dataset directly as input data for support vector machine and logistic regression resulted in poor classification indicators at certain magnitude thresholds. This was mainly due to the fact that the test data points, which were not of the same class at these thresholds, were only divided into one class. To address this issue, the dataset was standardized and normalized before being used as input. After comparing the experiment's results before and after normalization, it was found that the classification results had been improved to some extent.  
Figure 7 displays the ROC curves for the classification of large earthquake occurrences. Unlike the four evaluation indicators, the ROC curve can evaluate the model without requiring a threshold to be set, providing results that better reflect the true performance of  
Figure 6: Results of large earthquake occurrence classification: accuracy, precision, recall, and F1 score. A magnitude range of 5.0 to 7.0 is used to represent magnitude threshold of prediction.  
the model. Moreover, the ROC curve remains unaffected even when the distribution proportion of positive and negative samples in the test set changes. This feature is particularly important when dealing with category imbalances in actual datasets, as the ROC curve is able to effectively eliminate the impact of such imbalances on the evaluation results.  
An important measure derived from the ROC curve is the area under the curve (AUC). The closer the AUC value is to 1, the better the classification performance of the model. When the AUC is 0.5, the classification result is no better than random guessing. As depicted in Figure 7, the RF classifier achieved the highest AUC value of 0.98, indicating its superior performance in earthquake prediction. In contrast, the LR classifier had the lowest AUC value of 0.72, indicating the weakest performance among the four classifiers.