A machine learning method for the prediction of ship motion trajectories in real operational conditions

This paper presents a big data analytics method for the proactive mitigation of grounding risk. The model encompasses the dynamics of ship motion trajectories while accounting for kinematic uncertainties in real operational conditions. The approach combines K-means and DB-SCAN (Density-Based Spatial Clustering of Applications with Noise) big data clustering methods with Principal Component Analysis (PCA) to group environmental factors. A Multiple-Output Gaussian Process Regression (MOGPR) method is consequently used to predict selected ship motion dynamics. Ship sway is defined as the deviation between a ship and her motion trajectory centreline. Surge accelerations are used to idealise the time-varying manoeuvring of ships in various routes. Operational conditions are simulated by Automatic Identification System (AIS), General Bathymetric Chart of the Oceans (GEBCO), and nowcast hydro-meteorological data records. A Dynamic Time Warping (DTW) method is adopted to identify ship centre-line trajectories along selected paths. The machine learning algorithm is applied for ship motion predictions of Ro-Pax ships operating between two ports in the Gulf of Finland. Ship motion dynamics are visualised along the ship's route using a Gaussian Progress Regression (GPR) flow method. Results indicate that the present methodology may assist with predicting the probabilistic distribution of ship dynamics (speed, sway distance, drift angle, and surge accelerations) and grounding risk along selected ship routes.

• Record URL:
  • https://doi.org/10.1016/j.oceaneng.2023.114905
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0029801823012891
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2023 The Author(s). Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhang, Mingyang
  • 0000-0001-5820-2789
  • Kujala, Pentti
  • 0000-0003-2665-9957
  • Musharraf, Mashrura
  • Zhang, Jin-fen
  • 0000-0003-2703-6663
  • Hirdaris, Spyros
  • 0000-0002-4700-6325
• Publication Date: 2023-9-1

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: 114905
• Serial:
  • Ocean Engineering
  • Volume: 283
  • Issue Number: 0
  • Publisher: Pergamon
  • ISSN: 0029-8018
  • EISSN: 1873-5258
  • Serial URL: http://www.sciencedirect.com/science/journal/00298018

Subject/Index Terms

• TRT Terms: Bathymetry; Machine learning; Predictive models; Ship motion; Ship operations; Vehicle trajectories
• Geographic Terms: Gulf of Finland
• Subject Areas: Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01886108
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 28 2023 2:15PM