A novel method for ship carbon emissions prediction under the influence of emergency events

Abstract

Accurate prediction of ship emissions aids to ensure maritime sustainability but encounters challenges, such as the absence of high-precision and high-resolution databases, complex nonlinear relationships, and vulnerability to emergency events. This study addresses these issues by developing novel solutions: a novel Spatiotemporal Trajectory Search Algorithm (STSA) based on Automatic Identification System (AIS) data; a rolling structure-based Seasonal-Trend decomposition based on the Loess technique (STL); a modular deep learning model based on Structured Components, stacked-Long short-term memory, Convolutional neural networks and Comprehensive forecasting module (SCLCC). Based on these solutions, a case study using pre and post-COVID-19 AIS data demonstrates model reliability and the pandemic’s impact on ship emissions. Numerical experiments reveal that the STSA algorithm significantly outperforms the conventional identification standard in terms of accuracy of ship navigation state identification; the SCLCC model exhibits greater resistance against emergency events and excels in comprehensively capturing global information, thus yielding higher accurate prediction results. This study sheds light on the changing dynamics of maritime transport and its impacts on carbon emissions.

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ORCID for Huanhuan Li: orcid.org/0000-0002-4293-4763

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