Forecasting and Feature Analysis of Ship Fuel Consumption by Explainable Machine Learning Approaches

Abstract Rising shipping emissions greatly affect greenhouse gas (GHG) levels, so precise fuel consumption forecasting is essential to reduce environmental effects. Precision forecasts using machine learning (ML) could offer sophisticated solutions that increase the fuel efficiency and lower emissions. Indeed, five ML techniques, linear regression (LR), decision tree (DT), random forest (RF), XGBoost, and AdaBoost, were used to develop ship fuel consumption models in this study. It was found that, with an R² of 1, zero mean squared error (MSE), and a negligible mean absolute percentage error (MAPE), the DT model suited the training set perfectly, while R² was 0.8657, the MSE was 56.80, and the MAPE was 16.37% for the DT model testing. More importantly, this study provided Taylor diagrams and violin plots that helped in the identification of the best-performing models. Generally, the employed ML approaches efficiently predicted the data; however, they are black-box methods. Hence, explainable machine learning methods like Shapley additive explanations, the DT structure, and local interpretable model-agnostic explanations (LIME) were employed to comprehend the models and perform feature analysis. LIME offered insights, demonstrating that the major variables impacting predictions were distance (≤450.88 nm) and time (40.70 < hr ≤ 58.05). By stressing the most important aspects, LIME can help one to comprehend the models with ease.