Prediction of harbour vessel emissions based on machine learning approach

Zhong Shuo Chen, Jasmine Siu Lee Lam*, Zengqi Xiao

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Harbour vessel emissions are growing concerns in the maritime industry regarding environmental sustainability. Accurate emissions prediction can stand in monitoring and addressing the issue. This study proposes a machine-learning approach using Artificial Neural Network (ANN) for predicting harbour vessel emissions. The approach shows superiority over the bottom-up method introduced by the 4th IMO GHG Study regarding prediction accuracy. Actual emissions data from onboard measurements are used for training ANN models and as references for evaluating the methods. Compared to the bottom-up method, the improvement in error reduction can be up to 30% for predicting nitrogen oxides and 54% for carbon monoxide when only using ship-related factors as input variables. By adding selected meteorological factors in the experiments, the prediction accuracy enhancement can achieve up to 48% for nitrogen oxides and 62% for carbon monoxide. The proposed ANN approach could assist relevant stakeholders in improving emissions prediction and operations optimisation.

Original languageEnglish
Article number104214
JournalTransportation Research Part D: Transport and Environment
Volume131
ISSN1361-9209
DOIs
Publication statusPublished - 2024

Keywords

  • Artificial neural network
  • Carbon monoxide
  • Emission prediction
  • Harbour vessel
  • Machine learning
  • Nitrogen oxides

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