## 2 Materials and Methods
### RNN Autoencoder-Based Method for Extracting Capability Features
#### 2.2.2 Capability Feature Extraction Method  
(1) Model parameters  
Assume that Encoder is denoted by \(f\) in the feature extraction method shown in Figure 3, which is responsible for mapping the capability raw data \(x\) to the feature extraction layer \(a\); Decoder is denoted by \(d\), which is responsible for mapping \(a\) to the reconstructed features \(\hat{x}\); the number of samples of capability sequences for building weather disaster emergency subjects is \(N\), and the set of capability feature sequences can be determined. \[X=\left\{x_{1},x_{2},\cdots,x_{N}\right\} \tag{9}\]  
For the \(i\)th feature sequence  
\[x_{i}=\left(x_{i}^{(1)},x_{i}^{(2)},\cdots,x_{i}^{(T_{i})}\right) \tag{10}\]  
According to the RNN network structure, each node of the encoder-decoder architecture constructed in this paper is computed as follows. \[h_{i}=g\Big{(}W_{hh}h_{i}^{(l-1)}+W_{xh}x_{i}^{(l)}\Big{)} \tag{11}\]  
\[a_{i}=g\Big{(}W_{hh}h_{i}^{(T_{i})}\Big{)} \tag{12}\]  
\[h_{i}^{(t)}=g\Big{(}W_{h^{\prime}h^{\prime}}h_{i}^{(t-1)}\Big{)} \tag{13}\]  
Figure 3: The architecture of the autoencoder built in this paper. \[\hat{x}_{i}^{(t)}=g\Big{(}W_{h^{\prime}x}h_{i}^{\prime(t)}\Big{)} \tag{14}\]  
where \(\hat{x}_{i}^{(t)}\) is the reconstruction of the autoencoder for the input \(x\), and \(i\in\{1,2,\cdots N\}\), \(t\in\{1,2,\ldots,T_{i}\}\). \(h_{i}^{(0)}\) is the initial value of the capability feature, and a random number greater than or equal to 0 can generally be chosen as the initial value according to the connotation of the capability feature. \(h_{i}^{\prime(0)}=a_{i}\). \(W_{sh}\) denotes the input \(x\) to the connection weight matrix of the Encoder hidden layer \(h\). In the process of ability feature extraction, the mapping relationship between the original ability features and the output features can be obtained through the learning of this matrix, as shown in Equation (15). In this equation, \((w_{sh})_{ij}\) is the connection weight between the \(i\)-th dimensional input xi and the hidden state (capability feature) \(h_{j}\), which represents the contribution of \(x_{i}\) to \(h_{j}\). Unlike the general linear dimensionality reduction method, these weight effects are also affected by the activation function \(g\) of the activation function. \[W_{xh}= \begin{bmatrix}\left(w_{xh}\right)_{1,1}&\cdots&\left(w_{xh}\right)_{1,M}\\ \vdots&\ddots&\vdots\\ \left(w_{xh}\right)_{m,1}&\cdots&\left(w_{xh}\right)_{m,M}\end{bmatrix} _{m \times M} \tag{15}\]  
\(W_{hh}\), as shown in Equation (16), is the connection weight of the previous moment \(t-1\) hidden state of the Encoder part on the hidden state at moment \(t\). In the capability feature extraction, the influence of the previous capability information of the Encoder part on the current capability information is expressed, where \((w_{hh})_{ij}\) is the connection weight between the hidden state (capability feature) \(h_{i}\) at moment \(t-1\) and the hidden state (capability feature) \(h_{j}\) at moment \(t\), representing the contribution of \(h_{i}\) to \(h_{j}\). Again, these contributions are also affected by the activation function \(g\) of the \(h\). By learning this matrix, it is possible to retain useful information about the prior capabilities in the final extracted features. \[W_{hh}= \begin{bmatrix}\left(w_{hh}\right)_{1,1}&\cdots&\left(w_{hh}\right)_{1,m}\\ \vdots&\ddots&\vdots\\ \left(w_{hh}\right)_{m,1}&\cdots&\left(w_{hh}\right)_{m,m}\end{bmatrix} _{m \times m} \tag{16}\]  
\(W_{h^{\prime}h^{\prime}}\), as shown in Equation (17), is the connection weight of the previous moment \(t-1\) hidden state to the hidden state at moment \(t\) in the Decoder. The Decoder is able to reconstruct the capability data from the capability features compressed to \(a\). During the capability feature extraction, the \(W_{h^{\prime}h^{\prime}}\) denotes the influence of the Decoder's partial previous capability information on the current capability information. \[W_{h^{\prime}h^{\prime}}=