Explainable AI for ship design analysis with AIS and static ship data

Abstract

Decisions made in the early phases of ship design have a large influence on the capital and operational expenses of a vessel. In order to support decision making in this phase, big data and machine learning techniques can be of great use. This work shows how Explainable Artificial Intelligence (XAI) and Global Sensitivity Analysis (GSA) combined with Autonomous Identification System (AIS) and static ship data can be used to find important design characteristics of ships. A data collection framework is setup that collects AIS data over a five month time period. Static ship design data is used to predict performance related target features that are calculated from AIS data. By applying XAI and GSA methods to the regression models that predict these target features, insight can be gained on how design features influence the performance of ships. Experiments showed that for most ship types, the overall length is the most important design feature for speed related target features. Besides the overall length, the draught also has a significant impact on the rotation capabilities.