# Disaster Recognition and Classification Based on Improved ResNet-50 Neural Network
## 3 Results and Discussion
### Evaluation of Model Classification Performance
Notably, the model achieved perfect classification for avalanche and flood, with a normalized value of 1.00, and high accuracy for fire (0.95) and earthquake (0.90). However, relatively lower values were observed for landslides (0.65) and mudslides (0.80), suggesting minor confusion with other visually similar categories. These results reflect the model's strong ability to extract discriminative features across complex disaster categories, while also highlighting areas where further improvements in fine-grained classification may be necessary. Overall, the confusion matrix validates the model's robust and consistent performance under multi-class disaster recognition scenarios. Figure 6 illustrates the training and validation curves of the proposed disaster classification model over 50 epochs. As shown in the accuracy curve (Figure 6a), the training accuracy rapidly increases and stabilizes above 94% within the first few epochs. The validation accuracy also rises quickly during the early training phase, eventually stabilizing around 82% despite minor fluctuations, indicating consistent generalization capability. The loss curve (Figure 6b) further demonstrates that the training loss continuously decreases and remains at a low level throughout the process. In contrast, the validation loss shows slight oscillations but does not exhibit divergence, suggesting that no overfitting occurs. These patterns confirm that the model converges effectively and maintains a stable training process. The integration of the residual network structure facilitates efficient feature learning and alleviates the vanishing gradient problem, enabling the model to achieve robust performance with relatively few training epochs. Figure 4: (**a**) Overall prediction accuracy for all disaster types; (**b**) precision for each disaster type; (**c**) recall for each disaster type; (**d**) F\({}_{1}\)-score for each disaster type; (**e**) AUC for each disaster type; (**f**) log loss for each disaster type; and (**g**) specificity scores for each disaster type. sively extracts features from low-level to high-level representations, ultimately achieving classification through fully connected layers [27]. To comprehensively evaluate the effectiveness of the proposed ResNet-50-based model, comparative experiments were conducted against the traditional VGG16 model using the same disaster classification dataset. Figure 7 presents the performance comparison across six key evaluation metrics, including precision, recall, F1-score, AUC, specificity, and log loss. As shown, the proposed model consistently outperforms VGG16 in most metrics for the majority of disaster categories. In particular, it achieves higher average values in recall (0.87 vs. 0.86) and F1-score (0.87 vs. 0.87) and exhibits lower log loss (0.134 vs. 0.142), reflecting better prediction confidence and classification robustness. Additionally, the overall classification accuracy of both models  
Further insight is provided by the normalized confusion matrices in Figure 8, which depict the classification performance of both models on the validation set. The ResNet-50 model demonstrates stronger diagonal dominance, especially in categories such as flood, avalanche, and fire, indicating improved capability in correctly identifying various disaster types. In contrast, the VGG16 model shows slightly higher misclassification rates in categories like landslides and mudslides. These comparative results confirm that the proposed model achieves superior generalization and discrimination ability across diverse disaster scenarios. \begin{table}
\begin{tabular}{c c} \hline
**Model** & **Accuracy** \\ \hline ResNet-50 & 0.87 \\ VGG16 & 0.86 \\ \hline \end{tabular}
\end{table} 
Table 3: Overall accuracy comparison between models. Figure 8: (**a**) Normalized confusion matrix of the improved ResNet-50 model on the validation set and (**b**) normalized confusion matrix of the traditional VGG16 model on the validation set.