## 2 Materials and Methods
### RNN Autoencoder-Based Method for Extracting Capability Features
#### 2.2.1 RNN Autoencoder Architecture Design
The hidden state records the information of the current input and the historical state, which is a high summary and generalization of the historical input. The other nodes are calculated in a similar way to Equation (4). As can be seen from the figure, the encoder-decoder architecture of RNN consists of an encoder (Encoder) that reads the input sequence \(x\) (capability feature) and a decoder (Decoder) that produces the output sequence \(y\) (capability level). The state ht of the hidden layer is the intermediate result of encoding, which finally gets the encoded value of a (capability feature). Once the input sequence \(x\) has been processed by the Encoder through a forward computation, the Encoder produces a final hidden state vector \(a\) as its output. \(a\) is the temporal feature vector of the input sequence if _Wxh_, _Why_, and _Whh_ have been trained to be optimal. In machine translation problems, a is usually taken as the semantic implication of the input utterance, and a can be passed to the decoder for translation into another language sequence. \[a=g\left(W_{hh}h^{(T_{x})}\right) \tag{5}\]  
If the hidden state dimension is \(m\), then \(a=(a_{1},a_{2},\cdots,a_{m})\), \(g\) is the activation function, and to reduce the risk of avoiding gradient disappearance, ReLU is usually used as the activation function inside the RNN. From a temporal perspective, \(a\) is the result obtained by linear spatial mapping, nonlinear transformation, and weighting of time series data features, so learning by RNN \(a\) can accumulate the temporal information that is most meaningful for predicting y and attenuate the nonsignificant temporal information. From the standpoint of the feature, a more compact and refined capability information feature is created by reducing the dimensionality of the feature dimension, usually lowering the hidden layer dimension below the input dimension and removing redundant information from the feature dimension through data training. The self-coding mechanism produced by using RNN \(a\) can be used as an efficient extraction method for capability features by combining the roles of the two aforementioned perspectives. After performing the aforementioned analysis and looking at Figure 1, it is clear that the hidden states of earlier moments will influence later hidden states, but that this influence will wane over time. This is in line with the law that states that weather disaster emergency subjects build their capability features over time, allowing the final state to reflect the capability sequence features. The RNN-based encoder-decoder architecture is built to realize the dynamic extraction of capability sequence features when the data samples are sufficient. (2) Unsupervised feature extraction method  
As training samples for the architecture depicted in Figure 2, a sizable number of capability traits and capability level time series data of weather disaster emergency response capability subjects are needed. \[S=\{(x_{i},y_{i})\mid i=1,2,\ldots,N\} \tag{6}\]  
where \(S\) denotes the set of samples used to train the feature encoder, \(x_{i}\) is the weather disaster emergency subject capability feature data shown in Equation (1), and \(y_{i}\) is shown in Equation (3). However, due to the late start of meteorological disaster emergency management, there are still a large number of meteorological disaster emergency subjects that have not applied for capacity evaluation, i.e., there is no capacity level sequence \(y\). According to the relevant data from the network platform, as of December 2020, only the relevant network platform has saved the capacity-related data of 23,025 meteorological disaster emergency capacity subjects. In contrast, only 1249 capacity evaluation subjects (valid as of 2020) were officially announced in 2017. The number of subjects with multiple consecutive capability evaluation levels is low.