# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 5 Dam Deformation Prediction Model Based on Spatio-Temporal Correlation and IEALL
### Overall Framework of Model  
Based on IEALL model, a new spatio-temporal prediction model of dam deformation is proposed, which is based on STAGCN-IEALL model. To capture how this measuring point affects other measuring points in the space, GCN is utilized to extract the spatial properties of numerous measuring points. The IEALL model was utilized to extract the sequence's temporal properties, namely, to capture the measurement points' temporal dimension variation trend. At the same time, a spatio-temporal attention mechanism is introduced to optimize the model to deal with the dynamic correlation between different measuring points at any time and the dynamic correlation dependence of any measuring points at different time in the deformation sequence, so that the proposed algorithm can extract the spatio-temporal characteristics of the sequence more accurately and achieve more accurate prediction.  
The STAGCN-IEALL model framework, shown in Figure 4, consists mainly of two parts, the encoder network and the decoder network. The encoder module is composed of a spatio-temporal attention module, GCN spatial feature extraction module and IEALL temporal feature extraction module, and the decoder is composed of LSTM network. The specific process is as follows: Put a graph of the dam's deformation sequence at various measurement points into the encoder. Enter the spatio-temporal attention module, which is primarily divided into the spatial attention module and the temporal attention module, and compute the attention matrix under each of their respective dimensions; Following that, a normalization computation is used to produce a spatial attention matrix that contains spatial attention, and a temporal attention matrix that contains temporal attention, each value in the matrix indicates the attention allocation weight between nodes, in order to capture the dynamic correlation between geographical and temporal dimensions. Then, the matrix containing spatio-temporal attention output from the spatio-temporal attention module is inputted into the spatial feature extraction module, and GCN with a better spatial feature's extraction effect is used to extract spatial features from the input, spatio-temporal attention feature vectors are combined, spatial features from multiple measurement points are extracted, dynamic correlation between various measurement points at any particular time is also taken into consideration to obtain more complete spatial features. Similar to how the data are input into a temporal feature extraction module, the IEALL model is utilized to extract the features of the temporal dimension and combine the temporal attention feature vector, and more comprehensive temporal dimension features are obtained in consideration of the dynamic dependency of different moments. At the same time, the encoder outputs the hidden features of learning at each moment, and constructs a potential representation of the spatio-temporal features of the historical distortion sequence, namely spatio-temporal attention context vector. Finally, the LSTM decoder is used to decode according to the context vector and the output of the hidden layer, and the dam deformation sequence is reconstructed through a fully connected layer to obtain the final prediction value.