## 1 Introduction  
As a crucial social engineering infrastructure, dams must be operated safely to guarantee the needs of a steadily growing national economy are met. Unfortunately, due to the inherent physical limitations of dam materials, dams often have unhealthy structural responses such as dam body cracking and abnormal deformation [1]. In order to reduce the probability of engineering failures, most dams are equipped with precise health monitoring systems to evaluate their operational behavior and health through real-time measurements of multiple structural and environmental indicators. Among the many monitoring indicators, dam deformation is easy to measure and intuitively reflects the overall structural response state [2]. In order to improve the effectiveness of management strategies, research focused on accurately predicting dam deformation has increased in recent years. This area of research commonly uses simulations, and the most commonly used forecasting models can be categorized as mathematical statistical models or artificial intelligence models. Hydrostatic-seasonal-time (HST) can be considered a representative flagship statistical regression model, it quantitatively interprets the influencing factors behind dam deformation based on the assumptions of mechanical theory and, then, performs a linear approximation fitting using the observed data. It was originally proposed by [PERSON] et al. [3] to forecast deformation of concrete dams and has since been widely implemented. However, there is a strong correlation between dam water level and ambient temperature,which directly influences environmental loads and dam integrity, but the HST model does not consider local air temperature, which will be detrimental to the prediction accuracy under extreme weather conditions. To make up for these shortcomings, [PERSON] et al. [4] proposed the hydrostatic-seasonal-time-temperature (HSTT) model by correcting the thermal component based on the actual air temperature. Another common approach has been to replace the thermal component with the actual temperature inside dams, which prompted the advent of the hydrostatic-thermal-time (HTT) model. [PERSON] et al. [5] used a combined principal component analysis (PCA) method to include dam temperature in the model construction and applied the HTT model to explain the observed displacement of a concrete dam more accurately and with a lower residual standard deviation. In addition, there is a certain delay before a dam responds to changes in load, this can be observed in the influence of water level on pore water pressure and temperature on the thermal field of dams. The most popular solution is to add moving averages or gradients of original variables to the model to supplement the delayed information. For example, [PERSON] et al. [6] added the moving averages of air temperature for the previous 3, 10, and 30 days and the water level for the previous 3 days as variables to improve the performance of the dam deformation prediction model. Mathematical statistical models generally output linear relationships between impact factors and target variables. The coefficients are determined by the least squares method using a building process that is simple and easy to understand. However, in practice, the relationship between dam deformation and impact factors is rarely linear and the capacity of the above models to capture nonlinear features and generalize is insufficient. To address this deficit, artificial intelligence algorithms based on machine learning have gradually attracted more attention in dam deformation prediction. The application of artificial intelligence models in dam safety monitoring systems has since become another important research subject, using approaches such as support vector machine (SVM), random forest (RF), and gaussian process (GP). The machine learning algorithm captures the characteristics of observed data through specific algorithm steps and uses the extracted characteristic information to continuously update the model to achieve the best fit.