## 1 Introduction
Furthermore, most studies focus on improving the accuracy of predictive models, but ignore the interpretation and evaluation of input variables, which can be considered using deep learning models. To better consider the influence of time dimension, this paper coupled an attention mechanism with an LSTM network to develop a dam deformation prediction algorithm. The attention mechanism in the time dimension can preferentially allocate the limited information processing resources in the short term to key data, while the LSTM network can extract long-term change trends from dam deformation time series. This coupled model is able to obtain more accurate dam deformation prediction results while also enriching variable interpretation in the time dimension of the prediction model. During actual monitoring, the physical deformation sensor can be affected by environmental (external) factors or internal factors, which can produce abnormal data due to equipment error. Therefore, the density-based spatial clustering of applications with noise (DBSCAN) density-based clustering algorithm is introduced to eliminate equipment-based abnormal values in real time to ensure that the observed data meet the subsequent modeling accuracy requirements. Then, the relative importance of each input variable is obtained through the variable importance measure data processing method, which not only enriches the information interpretation of the model, but also screens redundant information to reduce the difficulty of modeling. The performance of the proposed model was verified by the real-world concrete gravity dam deformation data. The main contributions of this paper can be summarized as follows:  
1. This paper proposed and tested a DBSCAN method to filter the dam deformation time series data. The method effectively removed the equipment based abnormal values caused by environmental factors or equipment failures, thereby smoothing the random measurement errors in the observed data, which improved prediction accuracy. 2. The importance of input variables to the dam deformation prediction model was analyzed to interpret and evaluate the model. This resulted in a useful and efficient qualitative measure of dam deformation, which improved prevention and control of abnormal structural responses. 3. A coupled model was developed to better address the needs of dam deformation prediction. An attention mechanism focuses on the important variables in the short-term time dimension, while the LSTM model captures long-term change characteristics. This algorithm is very suitable for the prediction of dam deformation by accounting for time lag. The remainder of this paper is organized as follows: Section 2 describes the detailed process of the established mathematical model of dam deformation and the preprocessing method. Section 3 introduces the selection of input variables, and the design and operation of the comparative experimental model. Section 4 elaborates on a case study from which actual monitoring data was collected. Section 5 explains and analyzes the input variables and evaluates the performance of the proposed coupled method. Finally, conclusions and future research directions are provided in Section 6.