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This study incorporates two unique machine learning algorithms, Huber regression and Light Gradient Boosting Machines (LGBM), for estimating ship consumption of fuel. These methods are employed to create forecasting models for ship fuel consumption during journeys, which is especially useful when interacting with non-linear data. The study then analyzes and evaluates the prediction accuracy of these two approaches compared to a baseline model generated using linear regression. The results of the investigation show that both methods establish extremely accurate predictions while handling non-linear data quickly. However, the Huber-based model outperforms the LGBM in terms of prediction accuracy, with an R-squared value of 0.979 versus 0.917 for the LGBM. In addition, the Huber-based model has a diminished prediction error, with an RMSE of 2.278, compared to the LGBM model's RMSE of 4.55. The graphical methods of the violin plot and Taylor's diagram further established the superiority of Huber ML. These findings imply that Huber regression could be a suitable option for estimating in-route ship fuel usage in real time. As a consequence, this study emphasises the potential benefits of machine learning for accurately predicting ship fuel consumption, providing encouraging possibilities to optimise fuel usage while lowering greenhouse gas emissions.
Le et al. (Tue,) studied this question.