## 1 Introduction  
With the improvement of dam building technology, the ultra-high arch dam has reached the level of 300 m, e.g., the maximum dam height of Xiaowan in China is 294.5 m, the maximum dam height of Baihetan in China (under construction) is 289 m, the maximum dam height of Xliuodu arch dam in China is 285.5 m, and the maximum dam height of Enguri arch dam in the Republic of Georgia is 271.5 m [1, 2]. Due to the large volume, height, fluctuation range of water level, and complex deformation distribution under load, the ultra-high arch dam is an extremely complex nonlinear statically indeterminate structure. A large number of monitoring facilities are often required to monitor its operational state [3; 4]. The displacement of the dam is a direct indicator of the safety situation. During the rapid rise and fall of the reservoir water level, the influence of water pressure on dam deformation is especially prominent. Currently, determining the deformation law of an ultra-high arch dam is difficult and research is still needed [5; 6; 7]. Therefore, it is of great value to study the deformation law of ultra-high arch dams from both a safety and a theoretical point of view. Due to the high reading frequency of the instrument for monitoring the state of the dam, an automatic monitoring instrument achieves one to two measurements a day, so it is possible to accumulate a large amount of data. If all data are modeled and analyzed point by point, it is not only time- and labor-consuming, but also not conducive to mastering the operational behavior of the dam in time. Therefore, a reasonable method is needed to distinguish a wide range of the same type of measuring points, so as to select various representative measuring points for analysis which will ensure the time- and effectiveness of safety monitoring. At present, the prediction analysis method is usually a deformation time series of dam single or multiple measuring points combined with an intelligent algorithm [8; 9; 10; 11]. [PERSON] et al. developed a support vector regression identification model to predict the structural behavior of the dam [12], [PERSON] et al. proposed a wavelet support vector machine-based prediction model of dam deformation [13], [PERSON] et al. presented a prediction model of arch dam deformation via correlated multi-target stacking [14], and [PERSON] et al. analyzed the deformation and stress behavior of a high concrete dam under the effect of reservoir basin deformation [15]. The described methods can obtain highly reliable analysis results and prediction accuracy for the local range of general structures. In addition, the effects and monitoring of water levels on the dynamic properties of concrete arch dams have also been deeply studied [16; 17]. However, when using these methods for the monitoring analysis of an ultra-high arch dam the following problems often occur: (a) Due to the huge volume span, the water level fluctuates in both the amplitude and temperature gradient of an ultra-high arch dam. Given the comprehensive influence of various factors such as constraints and material properties, the deformation of different parts often varies greatly. If analyzed from a single time series, it cannot reflect the overall correlation state of the dam body. If a section of spatial measurement points is considered, the dynamic change trend of different measurement points is ignored. (b) When the monitoring data of a measuring point is wrong, or the data loss is serious due to instrument damage, the deformation analysis within a local range of measuring points can easily report a large error or wrong result. Feedback of this result to the dam operation state can easily lead to biased judgment, which cannot truly describe the deformation characteristics of an ultra-high arch dam. (c) When the monitoring time series is short, the linear correlation degree of deformation of measuring points at different parts of the dam is high.