## 3 Discussion on the Threshold of Covariate Introduction  
Reasonable selection of covariates affects the model's accuracy and computational efficiency, which is the key to constructing a cokriging spatial model. In order to ensure universality, the one-dimensional and the two-dimensional cokriging spatial model for different projects were constructed in this section. At the same time, the influence of different covariate combinations on the accuracy of the model was deeply analyzed, and the rationality of setting the VIP\({}_{j}\) control threshold of 0.8 was demonstrated. The error analysis of a single measuring point adopted the most commonly used cross-verification method [35]. \(N\) spatial models were obtained by sequentially leaving the measured values of each measuring point in a measurement time (\(n\) measuring points in total) as the verification set, and the measured value samples of the other \(n-1\) measuring points as the training set. The cross-validation results of all measuring points in the monitoring effect quantity were thus obtained, and the error analysis of each measuring point was carried out. Overall error analysis represented the cross-validation results of all measuring points in each measurement by calculating the mean absolute error (MAE). It analyzed the error  
Figure 2: Graph of covariance function. sequence of each measurement as a whole, as seen in Equation (9). The lower the MAE, the higher the overall accuracy. \[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|Z_{i}-Z_{i}^{*}\right|, \tag{9}\]  
where \(Z_{i}\) is the measured value of the measuring point \(i\); \(Z_{i}^{*}\) is the repaired value of measuring point \(i\). Analysis of the Influence of Covariates Selection on the Accuracy of the One-Dimensional Spatial Model  
A survey line was arranged at the elevation of 476.5 m upstream of a concrete face rockfill dam to monitor the vertical deformation of the surface, with a total of 10 measuring points, numbered SA6-1 to SA6-10. All measuring points are intact, and their arrangement and duration hydrograph are shown in Figures 3 and 4. The VIP method was used to identify the influencing factors of the effect variables of each measuring point. It was found that the important influencing factors of each measuring point were the same, aging and temperature had a great influence on vertical deformation, VIP\({}_{j}\) value exceeded 0.8 followed by water pressure, the VIP\({}_{j}\) value ranged from 0.25 to 0.64, and rainfall had the least influence; the VIP\({}_{j}\) value was less than 0.3, as shown in Figure 5.  
For the convenience of comparison and analysis, the aging single-factor, aging temperature two-factor, and aging temperature water pressure three-factor spatial cokriging models were constructed by using the measured values of each measuring point. The single-point error analysis was conducted for the measured values on 24 December 2020, as shown in Table 1. In the environmental variables, the aging is a monotonic increasing function, and the water level, temperature and rainfall are all periodic functions with years as the unit. Therefore, the overall error analysis was conducted for the measured values from January 2020 to December 2020 (due to COVID-19 pandemic, there was no measured value in February 2020), as shown in Figure 6. It could be seen that the repair accuracy of other measuring points was ideal, with absolute error within 5 mm and relative error within 3%, except for SA6-1 and SA6-10. Depending on the relative position of the points to be repaired in the group of known points, it can be divided into interpolation and extrapolation. Interpolation refers to repairing the unknown measuring points within the range, while extrapolation refers to repairing the unknown measuring points outside the range. The two measuring points SA6-1 and SA6-10 were located on the leftmost and rightmost banks in terms of spatial location.