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An application of machine learning to shipping emission inventory
Citation
Fletcher, T and Garaniya, V and Chai, S and Abbassi, R and Yu, H and Van, TC and Brown, RJ and Khan, F, An application of machine learning to shipping emission inventory, Royal Institution of Naval Architects. Transactions. Part A. International Journal of Maritime Engineering, 160, (A4) pp. A381-A396. ISSN 1479-8751 (2018) [Refereed Article]
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Copyright Statement
Copyright 2018 The Royal Institution of Naval Architects
Official URL: https://www.rina.org.uk/ijme.html
DOI: doi:10.3940/rina.ijme.2018.a4.500
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 Details
Item Type: | Refereed Article |
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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 |
Downloads: | 0 |
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