AN APPLICATION OF MACHINE LEARNING TO SHIPPING EMISSION INVENTORY

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Published: Dec 13, 2021
DOI: https://doi.org/10.5750/ijme.v160iA4.1073

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T Fletcher
National Centre for Maritime Engineering and Hydrodynamics, Australian Maritime College, University of Tasmania, Australia
V Garaniya
National Centre for Maritime Engineering and Hydrodynamics, Australian Maritime College, University of Tasmania, Australia
S Chai
National Centre for Maritime Engineering and Hydrodynamics, Australian Maritime College, University of Tasmania, Australia
R Abbassi
School of Engineering, Faculty of Science and Engineering, Macquarie University, Australia
R J Brown
School of Chemistry, Physics, and Mechanical Engineering, Queensland University of Technology, Australia
H Yu
School of Chemistry, Physics, and Mechanical Engineering, Queensland University of Technology, Australia
T C Van
School of Chemistry, Physics, and Mechanical Engineering, Queensland University of Technology, Australia
F Khan
Centre for Risk, Integrity and Safety Engineering, Faculty of Engineering & Applied Science, Memorial University of Newfoundland, Canada

Abstract

The objective of this study is to develop a shipping emission inventory model incorporating Machine Learning (ML) tools to estimate gaseous emissions. The tools enhance the emission inventories which currently rely on emission factors. The current inventories apply varied methodologies to estimate emissions with mixed accuracy. Comprehensive Bottom-up approach have the potential to provide very accurate results but require quality input. ML models have proven to be an accurate method of predicting responses for a set of data, with emission inventories an area unexplored with ML algorithms. Five ML models were applied to the emission data with the best-fit model judged based on comparing the real mean square errors and the R-values of each model. The primary gases studied are from a vessel measurement campaign in three modes of operation; berthing, manoeuvring, and cruising. The manoeuvring phase was identified as key for model selection for which two models performed best.

Article Details

Issue
Vol. 160 No. A4 (2018)
Section
Articles