## 3 Experiments and Analysis
### Performance Testing and Comparative Analysis
#### 3.3.3 Classifier Comparison
Moving beyond a mere quantitative analysis, it navigates away from the simple and anomalous samples, channeling its focus toward the more challenging, yet insightful data pockets [35]. This nuanced shift in focus not only nurtures a richer analytical depth but also engenders a more holistic understanding of the meteorological disaster capability spectra. A meticulous scrutiny of the model's performance metrics, namely Accuracy, F1 score, and AUC values, reveals its unparalleled superiority. It emerges as a method that is fine-tuned to navigate the complexities inherent in meteorological disaster capability assessments, embodying a harmonious blend of precision and depth. Furthermore, it would be remiss of the authors not to acknowledge the potential implications of this approach in crafting proactive and responsive disaster management strategies. Offering a more granular view of meteorological disaster capabilities equips policymakers and stakeholders with actionable insights, fostering a climate of informed decision-making and strategic foresight. Thus, as evidenced by its robust performance metrics and analytical depth, the Encoder-Adaptive-Focal deep neural network approach emerges as a pivotal tool in the realm of meteorological disaster capability assessments. It stands as a testament to the potential of leveraging advanced neural network techniques in forging pathways toward more resilient and adaptive disaster management frameworks. The degree of training accuracy is a pivotal parameter that reflects the model's attitude for effective learning from the provided training dataset. Throughout the course of this study, we meticulously monitored the training accuracy across various models to ensure a robust learning trajectory. Our Encoder-Adaptive-Focal model demonstrated an exemplary training accuracy, signifying its enhanced learning capability. We have elucidated the detailed training accuracy metrics in the revised manuscript, facilitating a clearer understanding of the models' learning efficacy and providing a foundation for the subsequent comparative analyses. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameters** & **Meaning** & **Value** \\ \hline _kernel_ & Kernel functions & RBF \\ _gamma_ & Coefficients of the kernel function & 0.33 \\ \(C\) & Penalty coefficient of the objective function & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Classifier hyperparameter settings for Encoder-SVM model. The F1 score and Area Under the Receiver Operating Characteristic Curve (AUC) are indispensable metrics for evaluating the performance of models, especially in scenarios characterized by imbalanced datasets, akin to our study. Our proposed Encoder-Adaptive-Focal model yielded an appreciable F1 score and AUC value, denoting its ability to adeptly handle sample imbalance and achieve reliable classification outcomes. The expanded discussion on these metrics within the revised manuscript offers a more nuanced comprehension of the models' effectiveness and their implications for the capability evaluation of meteorological disaster response agencies. We have enriched the comparative analysis to furnish a more holistic understanding of the relative merits and demerits of the proposed and existing models. The juxtaposition predicated on key performance metrics such as Accuracy, F1 score, and AUC values furnishes a comprehensive view of the models' performance. This augmented comparative analysis aims to bolster the justification for the results obtained, illuminating the superior performance of the Encoder-Adaptive-Focal model in addressing the cardinal challenge of sample imbalance. The implications of the models' performance transcend the theoretical ambit, venturing into the pragmatic domain of disaster management. The superior metrics exhibited by our proposed model underscore its potential in affording actionable insights to policymakers and stakeholders.