## 2 Materials and Methods
### Performance Analysis of Weather Disaster Emergency Response Capability Feature Extraction Method
A more complete time series sample can be created by combining these data. After data pre-processing, Table 3 provides statistics for a number of capacity features. Each time series sample must include more detailed capability characteristics and at least two records of capability rating in order to meet the criteria set forth by the capability rating model developed in this study, which eliminates samples with missing or incomplete data. \begin{table}
\begin{tabular}{p{42.7 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt}} \hline \hline  & \multicolumn{2}{c}{**Automatic**} & \multicolumn{2}{c}{**Hybaggnight**} & \multicolumn{2}{c}{**Wather**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Ratio of**} & \multicolumn{2}{c}{**Clean**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Organizational**} & \multicolumn{2}{c}{**Information**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Dississor**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Dississor**} & \multicolumn{2}{c}{**Command**} \\  & **Position** & **Station** & **Station** & **Control** & **Device** & **Guarantee** & **Control** & **Device** & **Guarantee** & **Control** & **State** & **Contraction** & **Reference** & **Emergacy** & **of** & **Dots per** & **Information** & **Staff** \\  & **Kate** & **Density** & **Coverage** & **Works** & **Initialization** & **Number** & **Sets** & **Status** & **Management** & **Regulations** & **Shelters** & **1000 People** & **Directional** & **Quality** \\ \hline Number of samples & 4052 & 4052 & 4052 & 4052 & 4052 & 4052 & 4052 & 4052 & 4052 & 4052 & 4052 & 4052 & 4052 & 4052 & 4052 \\ Average & 0.029 & 0.010 & 0.058 & 0.009 & 0.005 & 0.011 & 0.015 & 0.040 & 0.023 & 0.019 & 0.016 & 0.003 & 0.005 & 0.012 \\ Value & 0.048 & 0.034 & 0.079 & 0.045 & 0.038 & 0.049 & 0.059 & 0.061 & 0.082 & 0.000 & 0.038 & 0.023 & 0.035 & 0.031 \\ Minimum value & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ 25.1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ percentile & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ 50.1 & 0.016 & 0 & 0.043 & 0 & 0 & 0 & 0.008 & 0 & 0 & 0 & 0 & 0 & 0 \\ percentile & 750 & 0.036 & 0.006579 & 0.086 & 0 & 0 & 0 & 0.086 & 0 & 0 & 0 & 0 & 0 \\ Maximum value & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of some ability characteristics. Table 4 shows the information of the capacity rating of some entities in the past years, where \"N/A\" indicates that there is no rating or the capacity rating is missing in the current year. * is used to hide the specific name of the unit. AAA, AA, A, BBB, and CCC are the Evaluation criteria for emergency capability in Table 2.  
(3) The setting of Encoder dimension \(m\). According to the results of theoretical analysis in preceding part, scholars usually divide the connotation of competence into 2-5 aspects. For this reason, the dimension \(m\) of Encoder is normally set as 2-5, respectively. Through experimental comparison, the reconstruction accuracy of samples is higher when \(m\) is 3, and for this reason, the dimension \(m\) of Encoder's final state is set as 3. (4) Specific settings of hyperparameters. The encoder-decoder architecture is constructed and trained using the sample \(X\) of the characteristic sequences of building weather disaster emergency subject capabilities, and the specific settings of the hyperparameters are shown in Table 5.  
(5) Autoencoder code. The structure of the model is shown in Figure 4. The 24-dimensional features processed are converted to 3-dimensional features by Encoder as the input to the final state of feature extraction and mapped back to 24-dimensional by Decoder.