## 2 Related Work  
Monitoring models can be classified into statistical models, deterministic models, hybrid models, and intelligent algorithm models [4]. Statistical models that define the mapping between influences and measured influences as multivariate linear or multivariate nonlinear offer the benefits of computational efficiency and simplicity. [PERSON] et al. excluded the effect of aging on dam deformation and proposed a hybrid model that integrated linear and nonlinear factors to minimize the residual [12]. [PERSON] et al. proposed a hydraulic, hysteresis, seasonal, and temporal (HHST) model for the deformation of extra-high concrete dams [13]. However, the mechanisms of dams cannot be clearly explained using these models in terms of the evolution process, and the prediction accuracy is greatly degraded in the case of fewer monitoring data or more noise factors [14; 15]. Although deterministic models can clearly capture the behaviors of dam structures, it is difficult to promote these models in practical engineering due to the large number of simulation calculations [16]. Intelligent algorithmic models are characterized by high computational efficiency and the strong fitting ability of nonlinear mapping [17; 18]. [PERSON] et al. proposed a novel dam health monitoring model using optimized sparse Bayesian learning and sensitivity analysis, mainly for monitoring radial displacement and seepage [19]. [PERSON] et al. compared the results of support vector machines, artificial neural networks, and hybrid artificial intelligence models in dam displacement prediction [20]. [PERSON] et al. compared the outputs of the GPR-HST model with the GPR model and concluded that the GPR approach is more effective in capturing the nonlinearity of the dam response [21]. [PERSON] et al. proposed a novel method for predicting the displacement probability of concrete dams based on the optimized relevance vector machine (ORVM) [22]. However, intelligent models have high parameter sensitivity and are prone to overfitting [18]. The hybrid model combines structural simulation and statistics, which can better explain the mechanism of structural changes in dams, simplify the analysis difficulty, and is widely used in practical engineering. [PERSON] et al. combined artificial neural networks (ANNs) and the finite element-boundary element (FE-BE) model to predict the features of concrete gravity dams [7]. [PERSON] et al. proposed a model to separate the deformation of the dam body displacement from the dam foundation constraint displacement, in which the collected monitoring data were used to calibrate the zonal model [8]. [PERSON] et al. introduced a spatial-temporal hybrid model by considering the chaotic effect of the residual series to monitor the deformation of concrete arch dams [9]. [PERSON] et al. proposed a random coefficient model based on panel data, which solves the problem of multicollinearity of traditional regression methods by using time series and cross-sectional data [10]. [PERSON] et al. proposed a novel inversion framework that uses a surrogate model instead of the finite element model in the optimization loop [11]. The work of the above-mentioned scholars explores or improves one aspect of monitoring. As the service life of a structure increases, more and more costs need to be invested. Improvements in automation and reduced consumption have garnered the attention of scholars. A comprehensive scaling method of bridge structural deformation detection using multitemporal satellite-based differential interferometry is presented to automatically interpret the deformation of a specific bridge and prioritize the level of monitoring/assessment [23]. [PERSON] et al. implemented an online visualization system using WebGLto load and render models, but there are some limitations in terms of free user interaction, which affects the visualization effect to some extent [24].