## 3 Experiments and Analysis
### Performance Testing and Comparative Analysis
#### 3.3.3 Classifier Comparison  
To validate the suitability of implementing a deep neural network model for capability evaluation in this research, we compared the Encoder-Adaptive-Focal deep neural network capability evaluation model developed here with Encoder-DecisionTree, Encoder-SVM, and Encoder-NaiveBayes models in separate trials. The outcomes of these comparisons can be found in Table 10.  
The encoder parameters for the Encoder-DecisionTree model are specified in Table 7, with the classifier utilizing the CART model and hyperparameters outlined in Table 11.  
The Encoder-SVM model adopts the capability feature extraction methodology introduced in the previous paper. These extracted feature values serve as inputs for the random forest model during capability evaluation model training. Parameter settings for feature extraction mirror those in Table 7, while the classifier's hyperparameters adhere to Table 12. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models/Performance** & **Accuracy** & **F1 score** & **AUC** \\ \hline Encoder-Adaptive-Focal & 0.844 & 0.896 & 0.713 \\ Encoder-DecisionTree & 0.781 & 0.850 & 0.533 \\ Encoder-SVM & 0.776 & 0.849 & 0.526 \\ Encoder-NaiveBayes & 0.773 & 0.845 & 0.522 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance Comparison of Deep Neural Network and Traditional Models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparameters** & **Meaning** & **Value** \\ \hline _max\_depth_ & Maximum depth of the tree & 2 \\ _criterion_ & Conditions for delineating nodes & gini \\ _bootstrap_ & Self-help method & True \\ _min\_samples\_split_ & Minimum number of samples in the node dataset & 10 \\ _min\_samples\_leaf_ & Number of samples in leaf nodes at least & 6 \\ _max\_leaf\_nodes_ & The maximum number of leaf nodes the model can have & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Classifier hyperparameter settings for Encoder-DecisionTree model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models/Performance** & **Accuracy** & **F1 Score** & **AUC** \\ \hline Encoder-Adaptive-Focal & 0.844 & 0.896 & 0.713 \\ Adaptive-Focal & 0.819 & 0.862 & 0.641 \\ PCA-Adaptive-Focal & 0.801 & 0.865 & 0.628 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison results of dimensionality reduction methods. The Encoder-NaiveBayes model employs basic Bayes for capability assessment. Encoder parameters are set according to Table 7. The classifier is NaiveBayes, negating the need for hyperparameter tuning [34]. A review of the findings in Table 11 reveals that, for intricate classification tasks like the capability evaluation of meteorological disaster response agencies, deep neural network-based evaluation methods surpass traditional, mathematical, statistics-based approaches. Models such as Encoder-DecisionTree, Encoder-SVM, and Encoder-NaiveBayes not only exhibit reduced Accuracy and F1 scores compared to the Encoder-Adaptive-Focal method of this paper, but their AUCs also hover near 0.5. This suggests that mathematical, statistics-based evaluation methods might falter when faced with classification challenges involving imbalanced sample categories. Upon a comprehensive analysis and juxtaposition of data showcased in Tables 5, 8 and 9, a notable supremacy of the Encoder-Adaptive-Focal deep neural network approach in dynamic capability rating becomes palpable. This study underscores the model's proficiency at transcending conventional assessment paradigms. At its core, this method exhibits a heightened capability in the extraction of nuanced features from time series data, a critical step that facilitates a deeper insight into the evolving dynamics of meteorological phenomena. Notably, it adeptly compresses the dimensionality of these features, thereby alleviating computational burdens and paving the path for more streamlined analyses. Moreover, an ingenious aspect of this approach lies in its ability to recalibrate the focal point of the evaluation model.