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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.

Item Type:	Article
Authors/Creators:	Fletcher, T and Garaniya, V and Chai, S and Abbassi, R and Yu, H and Van, TC and Brown, RJ and Khan, F
Keywords:	shipping, air pollution, emission inventory, machine learning
Journal or Publication Title:	Royal Institution of Naval Architects. Transactions. Part A. International Journal of Maritime Engineering
Publisher:	Royal Institution of Naval Architects
ISSN:	1479-8751
DOI / ID Number:	10.3940/rina.ijme.2018.a4.500
Copyright Information:	Copyright 2018 The Royal Institution of Naval Architects
Related URLs:	Google Scholar: Garaniya, V UTAS Profile: Garaniya, V Google Scholar: Chai, S UTAS Profile: Chai, S
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