Zhang, M., Taimuri, G., Zhang, J. and Hirdaris, S. (2023) A Deep Learning Method for the Prediction of 6-Dof Ship Motions in Real Conditions. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 237, 887-905.
https://doi.org/10.1177/14750902231157852

has been cited by the following article:

• TITLE: Predicting Ship Propeller Speed with Multi-Source Data Fusion and Physics-Informed LightGBM: A Novel Correction Framework

  AUTHORS: Min Chen, Yingchao Gou, Feiyang Ren

  KEYWORDS: Ship RPM Prediction, Physics-Informed LightGBM, Multi-Source Data Fusion

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.13 No.4, September 24, 2025

  ABSTRACT: Accurate prediction of main-engine rotational speed (RPM) is pivotal for energy-efficient ship operation and compliance with emerging carbon-intensity regulations. Existing approaches either rely on computationally intensive physics-based models or data-driven methods that neglect hydrodynamic constraints and suffer from label noise in mandatory reporting data. We propose a physics-informed LightGBM framework that fuses high-resolution AIS trajectories, meteorological re-analyses and EU MRV logs through a temporally anchored, multi-source alignment protocol. A dual LightGBM ensemble (L1/L2) predicts RPM under laden and ballast conditions. Validation on a Panamax tanker (366 days) yields −1.52 rpm (−3%) error; ballast accuracy surpasses laden by 1.7%.