## 1 Introduction  
Dams have a long history in China as a comprehensive utilization project for power generation, flood control, irrigation, and water supply. Nowadays, because of various reasons, the risk of dam failure is increasing. For instance, extreme weather can increase the load acting on the dam, and material aging reduces durability [1]. Various monitoring equipment are typically installed in a dam to monitor its structural health at any time, to assess the multidimensional safety status of the dam, including environmental variables. Among the various structural responses of the dam, deformation is relatively simple to measure and can reliably reflect the overall service status of the dam [2]. The purpose of dam deformation monitoring is to maximize the use of the effective information of observation, so as to build a hazard warning model; with this model, the predicted value can be compared with the actual deformation to evaluate the changes in the dam performance. When the deviation exceeds a preset threshold, a risk alert is raised to identify the dangerous structure response promptly and avoid or reduce its impact [3]. Therefore, improving the accuracy of the dam deformation prediction model has important research significance in the field of dam safety monitoring. Dam deformation is affected by many factors, such as hydrological conditions around the project and its own material properties [4]. Therefore, it often presents the characteristics of diversity and time variation under the comprehensive influence of multiple factors [5]. In addition, the series of deformation data obtained during the process of construction and use of the dam tend to be highly volatile because of environmental factors and the performance of the collection equipment, including force majeure due to lightning strikes, heavy rains, and network environments. In short, the complexity of the dam deformation evolution law not only limits the accuracy of the prediction model to a certain extent but also increases the difficulty of building prediction models [6]. In the past few decades, many predictive models that can be roughly divided into three categories have been developed, namely physical models, conceptually lumped models, and data-driven models [7]. Generally, physical models help understand the process of dam deformation; however, building such models often requires a large-scale site, equipment, materials, and manpower. The conceptually lumped model involves constructing a numerical prediction model based on the dam structure and physical mechanism. However, the assumption and simplification of boundary conditions in the actual implementation process can avoid the complicated model building process but reduce the accuracy of model prediction, so making it unsuitable to accurately express the nonlinear characteristics of dam deformation [8]. Data-driven models typically use statistical models that involve hydrostatic-season-time (HST) modeling and hydrostatic-seasonal-time thermal (HSTT) modeling. They yield satisfactory prediction results without having to know the detailed mechanical information related to observation data, with specific physical explanations, high calculation speed, and a simple structure [9]. For dam deformation, conventional statistical models have been used to capture the complicated nonlinear characteristics in the past; yet, most statistical models define that the dam deformation is linear, which reduces their reliability and accuracy to a certain extent. Recently, owing to the gradual maturity of artificial intelligence, many data-driven artificial intelligence models have been extended to various complex dam monitoring problems, such as gaussian process regression (GPR) [10], tree-based ensemble models [11], the convolutional neural network (CNN) [12], and the long short-term memory (LSTM) network [13]. Fundamentally, the calculation process of an artificial intelligence model involves mapping some input variables to output variables.