## 1 Introduction
Through various complex processing operations, machine learning algorithms can obtain highly accurate predictive models that meet the management needs of safety monitoring. [PERSON] [7] introduced a prediction algorithm based on artificial neural networks (ANN) to map the relationship between the load and concrete dam deformation and compared it with the multiple linear regression (MLR) model. The results showed that the ANN model provided a better fit than the traditional statistical model under extreme temperatures. In addition, [PERSON] et al. [8] showed that the information provided by small static deformations can be enhanced by ANN-based methods. Furthermore, they developed a threshold level method for diagnosing the health of dams, and the impact of different factors on the health of dams was analyzed in detail. Recently, [PERSON] et al. [9] constructed an SVM nonlinear autoregressive model with exogenous inputs to predict the nonlinear behavior of a dam's structure. The safety measures protecting dams can be improved by being able to accurately predict the displacements of dams. [PERSON] et al. [10] demonstrated the accuracy of a dam deformation prediction model based on the GP method, which added the average air temperature and temperature lag information as input variables to predict the radial displacement of a concrete dam. In subsequent prediction comparisons, their GP model had the smallest error value. More recently, combinations of multiple machine learning algorithms have received increasing attention. [PERSON] et al. [11] used a fruit fly optimization algorithm to upgrade the SVM and applied it to the hysteresis correction of dam deformation impact factors. Subsequently, [PERSON] [12] proposed an SVM model with a wavelet based kernel function that made full use of the discrete transformation of the wavelet function. [PERSON] et al. [13] used the PCA method to extract the effective information from the dam temperature data as the input for the SVM model, effectively filtering redundant information from the input variables. However, the high-dimensional nonlinear tasks and the characteristic representation of time-varying dam deformation undoubtedly present a huge challenge for traditional shallow learning, meaning that the prediction accuracy of traditional machine learning algorithms is becoming increasingly unable to meet the needs of many engineering management tasks. In recent years, another branch of artificial intelligence technology, deep learning, has been vigorously developed in various industries, these approaches include convolutional neural networks (CNN), which are applied in image processing [14; 15; 16] and speech recognition [17; 18; 19], and long short-term memory (LSTM) models, which are applied in time series processing [20; 21; 22]. [PERSON] et al. [23] proposed an approach coupling PCA with LSTM to make short-term and long-term predictions of dam observation data. [PERSON] et al. [24] compared LSTM and SVM prediction algorithms for dam deformation monitoring. [PERSON] et al. [25] decomposed dam deformation time series into linear and nonlinear parts, then used traditional statistical models to fit the linear part, while LSTM was used to capture the sequence features of the nonlinear part. Deep learning uses a layered structure to embody abstract non-linear relationships and superimposes this structure to improve the expressive ability to map complex relationships. Each layer transfers information to another, with the output of the current layer being used as the input of the next layer, until the final output is obtained. After multiple layers of feature extraction and complex information representation, a sequence feature representation model can be obtained. This layered architecture makes deep learning highly customizable, allowing it to achieve a better prediction accuracy than traditional shallow learning.