基于面板数据模型的拱坝缺失数据填补方法OA北大核心CSTPCD
Panel data model-based method to fill in missing data for arch dams
混凝土拱坝作为重要的水工建筑物,由于监测设备故障、人为因素等影响,导致其监测数据频繁出现缺失的现象,降低了大坝安全评估与预测的有效性与准确性.传统方法多仅依赖单测点测值进行插补,忽略了测点之间的相关性与异质性.本文提出了一种基于面板数据模型的变形缺失数据插补方法.首先,改进传统变形相似性增量速度指标,解决了其分母可能等于零的问题.其次,提出了一种组合加权方法以计算变形相似性综合指标,并采用改进的基于密度聚类方法对变形监测点进行分类.随后,建立了面板模型,以填补不同区域内的缺失数据.本文提出的方法可以更准确地填补混凝土拱坝变形数据的缺失,从而能够有效地解决变形监测数据缺失的问题.
Concrete arch dams,as important hydraulic structures,frequently have missing measurement data due to monitoring equipment failures,human factors and other influences,which may reduce the effectiveness and accuracy of dam safety assessment and prediction.Previous methods mostly rely on single-point interpolation,neglecting the correlation and heterogeneity between measurement points.This paper develops a new method for interpolating the missing deformation data based on a panel data model.First,the incremental speed index of traditional deformation similarity is improved to solve the problem that its denominator may be equal to zero.Then,a combined weighting method is formulated to calculate a composite deformation similarity indicator,and an improved density-based clustering method is used to categorize the deformation monitoring points.Next,a panel model is developed to fill in the missing data in different intervals of the data sequence.This new method fills in the missing monitored deformation data of concrete arch dams more accurately,and thus can effectively solve the missing data problem.
崔欣然;石立;陆希;顾昊;吴艳;朱明远
河海大学 水利水电学院,南京 210098中国电建集团西北勘测设计研究院有限公司,西安 710065新疆水利水电科学研究院,乌鲁木齐 830049
水利科学
缺失数据填补变形相似性指标聚类方法面板数据模型混凝土拱坝
missing data imputationdeformation similarity indexclustering methodpanel data modelconcrete arch dams
《水力发电学报》 2024 (003)
94-107 / 14
国家自然科学基金(52379122);河海大学中央高校基本科研业务费项目(B230201011);新疆维吾尔自治区水利科技专项(XSKJ-2023-23)
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