## 6 Conclusions  
In this paper, aiming at the problems of the spatial models only considering the geometric position of measuring points, or the data repair mostly adopting time series models which may lead to poor accuracy, a spatial model for repairing dam safety monitoring data based on VIP-cokriging was proposed. The construction of the model included the steps of the decision of the construction dimension of the spatial model, selection of covariates, fitting of covariance function, and calculation of unrepaired value. Finally, the accuracy and applicability of the model were verified with engineering examples, and the following conclusions can be drawn:  
(1) Aiming at the problem of the spatial model for repairing dam safety monitoring data not considering the correlation with environmental variables, a cokriging spatial model based on the variable importance for projection was proposed. Firstly, the spatial model construction dimension was determined according to the spatial arrangement characteristics of similar monitoring instruments. Secondly, based on partial least squares regression, principal component analysis and typical correlation analysis were introduced to decompose and filter the information, and important environmental impact factors were identified and calculated as covariates. Finally, the weights of the same kind of intact measuring points and the weights of the covariates calculated by fitting the covariance function were brought into the model to calculate the repair value of the abnormal or missing measuring points. (2) Based on engineering cases of the one-dimensional and the two-dimensional models, the relationship between the accuracy of the VIP-cokriging model and the selection of covariates was analyzed. By comparing the repair accuracy of different covariate schemes chosen, the reasonableness of the VIP\({}_{j}\) control threshold setting of 0.8 was demonstrated. Choosing the environmental variable with the VIP\({}_{j}\) value greater than 0.8 to participate in the calculation could significantly improve the model's accuracy. Otherwise, it might increase the calculation amount and reduce the work efficiency without improving the model's accuracy. Moreover, the VIP-cokriging model has strong applicability and high accuracy at any time. (3) The VIP-cokriging spatial repair model can greatly improve the data repair effect and has the advantages of high precision and strong applicability. The application showed that VIP-cokriging effectively improved the accuracy of the spatial expansion of the main variables at the unknown point and reduced the error range at repair points by taking  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{3}{c}{**CH10**} & \multicolumn{3}{c}{**CH13**} & \multicolumn{3}{c}{**CH14**} \\ \cline{2-10}  & **Measured Value(mm)** & **Repaired Value(mm)** & **Relative Error** & **Measured Value(mm)** & **Repaired Value(mm)** & **Relative Error** & **Measured Value(mm)** & **Repaired Value(mm)** & **Relative Error** \\ \hline
29 January 2010 & 1789 & 1785.42 & 0.20\% & 2131 & 2124.25 & 0.32\% & 2011 & 2010.56 & 0.02\% \\ \hline
27 March 2010 & 1802 & 1797.24 & 0.26\% & 2146 & 2153.75 &  
into account the influence of important environmental variables and utilizing the cross-correlation between environmental variables and effect variables. Compared with the inverse distance weight model, the ordinary kriging model, and the universal kriging model, the repair error was reduced by 83%, 77%, and 67%. (4) Aiming at the problems of the damage of the monitoring points and the instability of the measured values at the profile \(0+240\) m of the PBG dam, a two-dimensional VIP-cokriging spatial model was adopted to extend the monitoring effect variables to the whole area of the dam space. The scientific correlation between existing monitoring points and the unknown spatial location of the dam was achieved, and the problem of repairing missing or abnormal data at key measuring points was solved.