## 3 Results
### Model Performance Comparison
To ensure linear separability of the classification problem, a linear kernel function was employed here. In the third step, the performance of the SVM classifier was assessed using the test set. Performance metrics, such as accuracy, precision, recall, and F1 score, were calculated. Finally, the performance of SVM classifiers based on different feature extraction methods was compared. Results are shown in Table 6.  
According to these indexes, the RNN autoencoder is superior to PCA-SVM, CPCA-SVM, and CPCA-RF methods in accuracy, precision, recall, and F1 scores. This indicates that the RNN autoencoder feature extraction method can provide higher-quality input for the evaluation of meteorological disaster emergency response capability, thus improving the evaluation accuracy and performance. There are several differences between the models that contribute to the superior performance of our proposed method. RNN autoencoders are capable of capturing temporal dependencies in time series data, which is crucial for analyzing and evaluating the dynamic capability sequences of meteorological disaster emergency response. In contrast, PCA and CPCA methods are \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline RNN Autoencoder & 0.68 & 0.70 & 0.72 & 0.71 \\ PCA-SVM & 0.49 & 0.51 & 0.53 & 0.52 \\ CPCA-SVM & 0.54 & 0.56 & 0.58 & 0.57 \\ CPCA-RF & 0.59 & 0.61 & 0.63 & 0.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison results of models based on SVM classification method. Figure 7: Comparison of the accuracy of K-nearest neighbor classification based on different feature extraction methods. based on linear transformations that might not be able to adequately capture the temporal structure of the data. Our RNN autoencoder method employs an encoder-decoder architecture that learns a compact representation of the input data while minimizing the reconstruction error. This allows for more efficient feature extraction compared to PCA and CPCA methods, which rely on orthogonal transformations that may not always lead to the most informative features. The RNN autoencoder can handle nonlinear relationships between input features, as it employs activation functions like ReLU to learn complex patterns in the data. This is in contrast to PCA and CPCA, which assume linear relationships between features. Lastly, our method benefits from the adaptability of RNNs, which can learn to focus on the most relevant features for the task at hand. This provides an advantage over PCA, CPCA, and random forest methods, which do not have the same level of flexibility in adapting to the specific requirements of the capability evaluation problem. In conclusion, our RNN autoencoder-based method shows promising results in extracting features for capability evaluation in the context of meteorological disaster emergency response. The superior performance of our method compared to the other methods is attributed to its ability to capture temporal dependencies, efficiently learn compact representations of the input data, handle nonlinear relationships, and adapt to the specific requirements of the problem. We believe that our approach has the potential to significantly improve capability evaluation in disaster management and response, and we plan to further refine and expand upon our method in future research.