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.