## 6 Conclusions  
The accuracy of the dam deformation prediction algorithm is vital to dam safety monitoring. This paper proposed a coupled LSTM/attention mechanism model that used GS and cross-validation methods to adaptively identify the most important parameters. Real world monitoring data of deformations of a concrete gravity dam were used as the research object to test the model and its performance was compared with several other advanced methods. The RF model was used to calculate the importance of input variables and any extraneous influencing factors were screened out while maintaining a sufficiently reasonable description of the dam deformation response. In addition, abnormal data points caused by various factors interfering with the monitoring equipment were eliminated using the DBSCAN method. According to this comprehensive study, the following conclusions were drawn:  
1. The results showed that the DBSCAN method is suitable for the detection of equipment based abnormal values. The processed data had an average value that was similar to that of the original data, but the variance and random errors were greatly reduced.
2. The RF model identified the most important variables needed to provide a reasonable explanation for dam deformation to be input into the model. The results revealed that the temperature was a particularly important factor in dam deformation, the  
Figure 14: Heat map of attention weight: (**a**) the attention mechanism is coupled before LSTM and (**b**) the attention mechanism is coupled after LSTM.  
importance of which was more than 50%, followed by water level, while the time component had the weakest influence.
3. The time-lag effect in dam monitoring plays an important role in predicting dam de-formation. When the model contained a time sliding window, the accuracy of the results was significantly improved, the residual distribution was relatively concentrated, and the outliers were greatly reduced.
4. The addition of the attention mechanism enabled the model to focus on important factors in the time dimension and improved the prediction of extreme values. The attention mechanism should be added before the LSTM layer to prevent the high-dimensional mapping of data processed by the LSTM layer from causing attention disorder.  
In the future, the effects of different normalization methods for input variables on the robustness of attention mechanism need to be discussed. Meanwhile, we will explore the dynamic effects of time lag and spatial characteristics of dam deformation to achieve coordinated dynamic predictions of dam deformation using multiple measurement points, which will analyze the dam safety as a whole and realize data reduction.  
[PERSON] conceived and designed the experiments and performed the modeling. [PERSON] provided relevant technical support and produced the first draft. [PERSON] and [PERSON] analyzed the data and reviewed and edited the paper. All authors discussed the basic structure of the manuscript and read and approved the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Young Scientist Program of Fujian Province Natural Science Foundation (Grant No. 2020J05108), and Talent Introduction Scientific Start-up Foundation of Fuzhou University (Grant No. 510890).  
The authors declare no conflict of interest.