ArticleScientific reports2025
Real-time emission prediction for ship engine using stacked time-series learning: a transformer-XGBoost hybrid framework.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Global trade is mainly conducted via maritime transportation; however, exhaust emissions from vessel diesel engines significantly contribute to air pollution and global warming. Existing emission inventory method, such as bottom-up or top-down approaches proposed by the International Maritime Organization and European Environment Agency, are limited in reflecting dynamic characteristics of actual engine operation. This study proposes a hybrid prediction model that integrates a time-series forecasting transformer and XGBoost using real-time engine operational data. Key influencing variables are selected through least absolute shrinkage and selection operator regression. The proposed model enhances emission prediction accuracy across various operating conditions. Compared to conventional methods, root mean square error and mean absolute error were reduced by 34% and 40% for CO
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