## 1 Introduction
If the modeling analysis is carried out directly, the reliability of the results is often low due to the low degree of freedom of the data. In view of the above problems, the panel data clustering theory [18] is applied and transforms the dam single point measurement point analysis method into an area analysis method. This method comprehensively considers the deformation characteristics of the dam space-time measurement points and can fully mine the deformation monitoring information in the two dimensions of time and section. Previous papers have researched the deformation application of clustering algorithms and the application of panel data theory. [PERSON] et al. used a fuzzy clustering-based model to estimate of deformation modulus of rock masses [19], [PERSON] et al. built a DP-GMM clustering-based ensemble learning prediction methodology to study dam deformation [20], [PERSON] et al. proposed a clustering method for panel data based on adaptive weighting [21], [PERSON] et al. studied quantile-regression-based clustering for panel data [22], [PERSON] et al. applied panel data to achieve a three ways decisions based grey incidence analysis clustering approach on the regional high-tech industrialization [23], and [PERSON] et al. proposed a mixed-coefficientpanel model for evaluating the overall deformation behavior of dams [24]. It can be seen from the above research that the clustering algorithm has strong applicability and accuracy in analyzing structural engineering deformation. Panel clustering analysis has been well applied in environment, agriculture, and engineering fields, and needs further development in the application of dam deformation. Because the distance of panel clustering analysis is the core index to determine the clustering, the displacement is often divided into \"absolute\" and \"increment\", ignoring the fluctuating displacement of dam deformation with the accompanying effect of water level. Therefore, the calculation method of fluctuation distance in arch dam displacement is proposed to adapt to the complex water level condition of the dam. Due to the large variation of the comprehensive distance in the process of dynamic clustering, the CRITIC method is used to calculate the objective weight coefficient of the comprehensive distance of clustering [25]. This is done to balance the local and global optimization. This method not only considers the amount of information of evaluation indexes but also considers the discrimination and conflict between evaluation indexes. In a large number of research applications, the CRITIC method has high result accuracy and strong sensitivity to evaluation indicators. The CRITIC method has a high ability to optimize and integrate the evaluation information, especially when the database is large and there are many influencing factors [26]. The CRITIC method has been well applied and studied in many fields involving statistics, such as process improvement in aluminum alloys [27], software selection problems [28], and intelligent healthcare management evaluations [29]. In the cluster analysis of dam deformation, the number of cluster partitions is one of the key indexes to evaluate the results [30; 31]. Generally, the clustering algorithm automatically calculates the number of clusters according to the characteristics of the database and its algorithm, which makes the number of clusters difficult to control and consistent in different databases [32]. Under different water level conditions, the number of partitions of the clustering algorithm is based on panel data and the CRITIC method is sensitive to the database. If the number of clusters is very small, the deformation partition has no discrimination. If the number is very large, the significance of cluster analysis will be lost. If the number of clusters is inconsistent, the results cannot be compared and analyzed. For these three cases, it is difficult to further analyze the deformation characteristics of the dam.