AI-Based Interpolation of Ship Dry Docking Operation Data

Among the vessel operation data collected worldwide, static data represent a vessel’s inherent specifications. However, due to technical limitations and environmental factors, a significant portion of these data may contain missing values. In particular, dry docking operations are fundamental process for ensuring safe navigation through vessel maintenance, and the amount of paint discharge generated during ship repairs has traditionally been estimated using static data. However, dry docking data frequently contain a high proportion of missing values, which undermines the reliability of analytical results. To address this issue, this study proposes an AI-based methodology to impute missing static data for vessels engaged in dry docking operations using the Automatic Identification System (AIS). The proposed approach employs an AI-driven imputation method based on the MissForest algorithm, applied to data collected from shipyards in South Korea. This method was used to impute key vessel attributes, including length, breadth, and draft. The reliability of the imputed data was statistically validated through comparisons with baseline models using the Mann–Whitney U test and the Kolmogorov–Smirnov test. As a result, 473 missing values were successfully imputed, expanding the dataset to 1688 dry docking records.