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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:	Refereed Article
Keywords:	shipping, air pollution, emission inventory, machine learning
Research Division:	Engineering
Research Group:	Maritime engineering
Research Field:	Marine engineering
Objective Division:	Environmental Management
Objective Group:	Air quality, atmosphere and weather
Objective Field:	Air quality
UTAS Author:	Fletcher, T (Mr Tom Fletcher)
UTAS Author:	Garaniya, V (Associate Professor Vikram Garaniya)
UTAS Author:	Chai, S (Professor Shuhong Chai)
ID Code:	129767
Year Published:	2018
Web of Science® Times Cited:	2
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2018-12-18
Last Modified:	2019-05-14
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