# Disaster Recognition and Classification Based on Improved ResNet-50 Neural Network
## 3 Results and Discussion
### Evaluation of Model Classification Performance  
For the multi-class disaster classification task, this study computed the accuracy (Equation (18)), precision (Equation (19)), recall (Equation (20)), and F\({}_{1}\)-score (Equation (21)) for each disaster category and used macro-averaging to evaluate the overall model performance. Table 2 presents the classification report of the proposed model on seven natural disaster categories in the test set. As shown in the table, the classification performance varies slightly across categories. The highest F\({}_{1}\)-score of 0.95 was achieved in the \"avalanche\" and \"fire\" categories, while the lowest F\({}_{1}\)-score (0.74) appeared in \"landslides\". The overall accuracy on the test set reached 87%, indicating satisfactory classification capability. Furthermore, to provide a more comprehensive evaluation of the model's performance, additional metrics, including the area under the ROC curve (AUC), logarithmic loss \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Disaster Type** & **Precision** & **Recall** & **F1-Score** & **AUC** & **Specificity** & **Log Loss** & **Support** \\ \hline Land subsidence & 0.89 & 0.80 & 0.84 & 0.94 & 0.96 & 0.175 & 20 \\ Landsides & 0.87 & 0.65 & 0.74 & 0.91 & 0.95 & 0.210 & 20 \\ Avalanche & 0.91 & 1.00 & 0.95 & 0.99 & 1.00 & 0.045 & 20 \\ Earthquake & 0.90 & 0.90 & 0.90 & 0.95 & 0.98 & 0.080 & 20 \\ Fire & 0.95 & 0.95 & 0.95 & 0.98 & 0.998 & 0.050 & 20 \\ Flood & 0.87 & 1.00 & 0.93 & 0.99 & 0.99 & 0.060 & 20 \\ Mudslide & 0.73 & 0.80 & 0.76 & 0.93 & 0.94 & 0.180 & 20 \\ Total/Average & 0.87 & 0.87 & 0.87 & 0.95 & 0.96 & 0.134 & 140 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model classification report. (Log Loss), and specificity, were calculated. In the classification report, the term support refers to the number of ground truth instances used to evaluate each class. The model achieved an average AUC of 0.957, an average specificity above 0.96, and a log loss of 0.134, demonstrating its robust discrimination ability, high confidence in probabilistic outputs, and strong resilience to false positives. These metrics further confirm the effectiveness and generalization ability of the improved ResNet-50 model in multi-category disaster image classification tasks. Here, Figure 4 further provides a visualized supplement to the quantitative results presented in Table 2. The figure depicts the distributions of precision, recall, F\({}_{1}\)-score, AUC, specificity, and log loss across the seven disaster categories. In addition, the overall classification accuracy of the model is also included in the visualization. This comprehensive depiction facilitates a clearer and more intuitive understanding of the model's performance on each disaster type as well as its general classification capability. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{18}\]  
\[Precision=\frac{TP}{TP+FP} \tag{19}\]  
\[Recall=\frac{TP}{TP+FN} \tag{20}\]  
\[F_{1 score}=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{21}\]  
where _TP_ (True Positive) is the number of correctly predicted positive samples for a given class; _TN_ (True Negative) is the number of correctly predicted negative samples (non-class samples); _FP_ (False Positive) is the number of incorrectly predicted positive samples (non-class samples misclassified as the target class); and _FN_ (False Negative) is the number of incorrectly predicted negative samples (target class samples misclassified as other classes). In addition to the six evaluation metrics discussed previously, a normalized confusion matrix was constructed to further analyze the classification performance of the proposed ResNet-50-based model on the validation set. As illustrated in Figure 5, the matrix demonstrates that the majority of predictions are concentrated along the diagonal, indicating a high degree of accuracy in classifying most disaster types.