乏数据条件下土石坝安全监测数据的插补  

Imputation of safety monitoring data for earth-rock dams under sparse data conditions

作　　者：赵瑞桥 李登华[1,2] 石北啸 ZHAO Ruiqiao;LI Denghua;SHI Beixiao(Nanjing Hydraulic Research Institute,Nanjing 210029,China;Key Laboratory of Reservoir Dam Safety,Ministry of Water Resource,Nanjing 210029,China)

机构地区：[1]南京水利科学研究院,江苏南京210029 [2]水利部水库大坝安全重点实验室,江苏南京210029

出　　处：《水利水运工程学报》2025年第2期144-152,共9页Hydro-Science and Engineering

基　　金：国家重点研发计划资助项目(2022YFC3005502);国家自然科学基金资助项目(52279135);江西省水利厅重大科技项目(202124ZDKT06)。

摘　　要：水库大坝安全监测资料应及时整编分析,以便通过监测资料及时了解大坝性状,并为大坝总体安全评价提供基本资料。传统的大坝缺失数据补全方法依赖于完整的前置数据和经验基函数,这对数据缺乏的中小型土石坝效果不佳。利用经验模态分解算法分析缺失测点和同源测点数据,可从较少的数据中提取有效信息。针对不同复杂度下分解得到的分量不统一问题,利用动态时间调整算法进行聚类整合。最后对聚类数据集分别建立基于门控循环单元的预测模型,构建乏数据下历史监测数据EMD-GRU填补算法。基于实际工程监测数据对该算法和传统算法进行对比发现,均方误差降低至0.6以下,在乏数据的背景下该算法比传统模型有更好的稳定性和泛化性。Safety monitoring data for reservoir dams must be promptly compiled and analyzed to gain real-time insight into dam conditions and to provide essential data for overall safety evaluations.Traditional methods for imputing missing dam data rely on comprehensive pre-existing data and empirical basis functions,which are ineffective for small and medium-sized earth-rock dams with limited data.By utilizing the Empirical Mode Decomposition(EMD)algorithm to analyze missing and homologous monitoring points,effective information can be extracted from minimal data.To address the inconsistency in decomposed components of varying complexity,Dynamic Time Warping(DTW)is employed for clustering and integration.Finally,a prediction model based on the Gated Recurrent Unit(GRU)is built for each clustered dataset,forming an EMD-GRU imputation algorithm for historical monitoring data under sparse data conditions.A comparison between this algorithm and traditional methods,using real-world monitoring data,shows that the mean squared error decreases to below 0.6,demonstrating improved stability and generalization over traditional models in sparse data environments.

关 键 词：土石坝 安全监测 数据填补 乏数据 

分 类 号：TV641[水利工程—水利水电工程]