A Spatial Model for Repairing of the Dam Safety Monitoring Data Combining the Variable Importance for Projection (VIP) and Cokriging Methods

Abstract

The safe operation of dams is related to the lifeline of the national economy, the safety of the people, and social stability, and dam safety monitoring plays an essential role in scientifically controlling the safety of dams. Since the effects of environmental variables were not considered in conventional monitoring data repairing methods (such as the single time series model and spatial interpolation model), a spatial model for repairing monitoring data combining the variable importance for projection (VIP) method and cokriging was put forward in this paper. In order to improve the accuracy of the model, the influence of different combinations of covariates on it was discussed, and the VIPj value greater than 0.8 was proposed as the threshold of covariates. The engineering verification shows that the VIP-cokriging spatial model had the advantages of high precision and strong applicability compared with the inverse distance weighting (IDW) model, the ordinary kriging model, and the universal kriging model, and the overall error can be reduced by more than 60%, which could better realize the expansion of the monitoring effect variable to the whole area of the dam space. The engineering application of the PBG dam showed that the model scientifically correlated the existing monitoring points with the spatial location of the dam, and reasonably repaired the measured values of the stopping and abnormal measured points, effectively ensuring that the spatial regular of the monitoring data could truly reflect the actual safety and operational status of the dam.

AIoT Monitoring Technology for Optimal Fill Dam Installation and Operation

“…In recent years, various techniques, such as advanced photogrammetry, digital twin, GNSS, deep learning algorithms, and numerical methods [7][8][9][10], have been reported. Lee [11] constructed a 3D model for the Malpasset Dam located in southern France by introducing short-range photogrammetry and observed the dam failure geometry using the model.…”

Structural Health Monitoring: Latest Applications and Data Analysis

“…The results showed satisfactory linear correlation coefficients and low absolute and relative errors, confirming the effectiveness of the random forest algorithm for evaluating the moisture content of brick walls without destructive intervention. Advanced data processing techniques are also used in the research presented in article [5], where the authors emphasize the importance of continuous and reliable dam safety monitoring data for the evaluation and control of dam operation and security. The article proposes a cokriging spatial model based on variable importance for data repair, considering the spatial and environmental factors that influence dam safety.…”