A Novel Method for Imputing Missing Values in Ship Static Data Based on Generative Adversarial Networks

Abstract

Ship data obtained through the maritime sector will inevitably have missing values and outliers, which will adversely affect the subsequent study. Many existing methods for missing data imputation cannot meet the requirements of ship data quality, especially in cases of high missing rates. In this paper, a missing data imputation method based on generative adversarial networks (GANs) is proposed. The generative adversarial imputation network (GAIN) is improved using the Wasserstein distance and gradient penalty to handle missing values. Meanwhile, the data preprocessing process is optimized by combining knowledge from the ship domain, such as using isolation forests for anomaly detection. Statistical analysis of ship data is also conducted, including correlation analysis of ship design parameters, analysis of outliers, and analysis of missing data types. These analyses provide the basis for the proposed model. In a case study of 8167 bulk carriers, the proposed model outperformed the missing forest (MF) and polynomial fitting (PF) models, with an average error reduction of 2.4% and 6.3%, respectively. The proposed model also showed stable performance in cases of high missing rates. This study provides a new approach for estimating or imputing critical parameters of ships.