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Lang, X., Wu, D. and Mao, W. (2024) Physics-Informed Machine Learning Models for Ship Speed Prediction. Expert Systems with Applications, 238, Article ID: 121877.
https://doi.org/10.1016/j.eswa.2023.121877

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%.