基于Apriori和GP-XGBoost的特高拱坝变形缺失数据填补方法

变形监测数据作为特高拱坝服役性态最直观的表征，蕴藏着丰富的时空信息和演变规律，对工程长治久安意义重大。然而，多源多维的变形监测数据受仪器本身及外界因素影响，往往存在数据缺失的现象，会对接下来的数据分析工作造成干扰。针对大坝变形监测序列中的缺失数据，基于Apriori关联规则算法挖掘测点变形在空间维度上的关联性，得到目标测点的强关联测点，随后以强关联测点的变形监测数据作为输入样本，利用贝叶斯优化的XGBoost回归模型填补了目标测点的空缺变形监测序列。结合锦屏一级特高拱坝工程实例表明，该填补方法实现了变形监测空缺信息的高效、精准填补，可用于类似大坝工程的变形缺失数据填补。

Missing data filling method of ultra-high arch dam deformation based on Apriori and GP-XGBoost

Deformation monitoring data can reflect the service behavior of ultra-high arch dams directly, they contain abundant spatio-temporal information and evolution law, which is of great significance to the long-term stability of the project. However, the multi-source and multi-dimensional deformation monitoring data are often affected by the monitoring instrument itself and other external factors, which will cause interference to the following data analysis. Regarding to the missing data in dam deformation monitoring sequence, the target monitoring sites with strong correlation were obtained based on the spatial correlation of monitoring site deformation calculated by Apriori association rule, and then the deformation monitoring data were adopted as the input samples to fill in the gaps in deformation monitoring sequence of the target monitoring sites using Bayesian optimized XGBoost regression model. The case study of Jinping ultra-high arch dam shows that this method can fill in the deformation monitoring gaps efficiently and accurately, so it is applicable to the filling of missing deformation data of similar dam projects.