## 3 Experiments and Analysis
### Model Construction  
The capability evaluation of meteorological disaster emergency response agencies encompasses two primary components: capability feature extraction and evaluation model construction [29]. Building on this foundation, a more comprehensive capability evaluation model construction phase is developed, integrating the model extraction steps delineated in the previous section.  
1. Capability Feature Encoding: As detailed in Equations (3)-(9), the final encoder is attained by marrying the SGD algorithm with the training of an Encoder-Decoder architecture based on the ability feature sequence set \(X^{\prime}_{f}\).
2. Capability Feature Extraction: In alignment with Equation (10), feature extraction is conducted on \(X\) to realize the objective of learning the dynamic features of the sequence and reducing dimensionality, thereby deriving the new feature set \(A\) and the dimensionality-reduced data set \(S^{\prime}=(A,Y)\).
3. Dataset Partitioning: Following a predetermined ratio, the dataset \(S^{\prime}\) is bifurcated into a training set \((S^{\prime}_{\text{train }})\) and a testing set \((S^{\prime}_{\text{test }})\).
4. Model Parameter Setting: Guided by existing domestic and international research, along with insights from authoritative experts, the range for hyperparameter values is established, as delineated in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparameters** & **Meaning** & **Role** & **Range of Values** \\ \hline \multirow{2}{*}{_Layers_} & Number of neural network layers & Change the complexity of the model & [4; 7]. \\  & Number of neurons in layer \(l\) & Change the complexity of the model & [4; 8]. \\  & The activation function of the & & [Relu, Tanh, Sognoid, Softmax] \\  & \(l\)th layer & Learning non-linear relationships & [Relu, Tanh, Sognoid, Softmax] \\  & Batch size of input samples per & Trade-off between model accuracy & [16; 32; 64] \\  & training round & and training efficiency & [16; 32] \\  & & Make the model continuously & \\  & Number of model training rounds & learn the distribution of the training data & [100; 500] \\  & & training data & Control the step size of the weight update & \\ \(\varepsilon\) & Learning Rate & & [0.001, 0.005, 0.01, 0.05] \\  & & Automatically adjust the learning rate according to some strategy to find the global optimal point & [SGD, AdaGrad, RMSProp, Adam] \\  & & Correction factor & Control \(diff_{i}^{(\varepsilon)}\) for \(s_{i}\) The correction magnitude of & [0.2, 0.5, 0.8] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Value range of hyperparameters.  
Figure 3: Scenario where _TPRate_ is smaller than _FPRate._Utilizing Equation (8), where \(L_{\textit{a}-Adaptive-Focal}(p_{i},y_{i})\) functions as the loss function, and in accordance with Equations (9)-(17), we construct and train a deep neural network based on \(S^{\prime}_{\text{train}}\) to develop the Encoder-Adaptive-Focal-based deep neural network capability evaluation model. Employing this model, predictions are executed on \(S^{\prime}_{\text{test}}\). The performance indices of the capability evaluation model are ascertained following Equations (18)-(21), facilitating a comprehensive assessment of its efficacy. Figure 4 illustrates the modeling workflow of the proposed capability evaluation scheme.