## 1 Introduction
### Literature Review
Finally, a prediction model was developed using the Random Forest Regressor model to determine the required shaft power based on oceanographic factors and manoeuvre settings, and the SHapley Additive exPlanations (SHAP) method was implemented to provide insights into the learning process of the model and therefore offer explainability on the final outputs. Finally, ref. [16] investigated the prediction of hull performance using MLP and convolutional neural network (CNN) models. Fuel consumption prediction is another key area of research. In [17], three machine learning methods--the White, the Black and the Gray Box models--were used to forecast fuel consumption and optimise trim for a Handymax chemical/product tanker using operational data. Similarly, ref. [18] used operational data from a single Pogoria ship to predict fuel consumption and speed with an ANN. In [19], the LASSO method was applied to predict fuel consumption under varying sea states and weather conditions using data from 97 container ships for a period of 3.5 years. The authors in [20] applied a data-driven approach with multiple Linear Regression to predict fuel consumption for bulk carriers, focusing on the impact of weather factors like wind, waves, and currents. In another study [21], two algorithms--Huber Regression and Light Gradient Boosting Machines (LGBMs)--were employed to estimate fuel consumption, emphasising the advantages of machine learning in accurately predicting fuel consumption as a valuable opportunity to optimise fuel use and reduce greenhouse gas emissions. Moreover, ref. [22] applied Multiple Linear Regression (MLR), Ridge Regression (RR), LASSO Regression, and Support Vector Regression (SVR) to predict fuel consumption, comparing their accuracy using error metrics. In another study [23], an ANN and Gaussian Process Regression (GPR) were used to forecast fuel consumption, while [24] found that ANNs outperformed MLR in predicting container ship behaviour. In their study, the authors used in-service data from a 13.000 TEU class container ship from a six-month period. Similarly, ref. [25] employed ANNs for predicting fuel consumption and speed in container ships. Finally, ref. [26] utilised SVM, RF, Extra Tree (ET) and ANNs to predict fuel consumption based on non-reports. Another interesting research area involves resistance prediction. In [27], the authors developed a model to predict the total resistance of bare-hull sailboats under uniform draft conditions based on machine learning. They applied a Regression Tree, an SVR, and an ANN using data from three sailboats' systematic series (Delft, US Sailing, and Il Moro di Venezia). This model helps assess the resistance of sailboat hulls, especially during the first design stages. A more specialised approach was developed in [28] with a focus on ice-covered water, where an ANN model was designed to estimate ship resistance in these conditions utilising both ship design parameters and ice-mechanic properties. Other research papers focus on speed prediction. For example, author from [29] investigated various regression techniques, such as LR, Regression Trees (RTs), both single and ensembles, Gaussian Process Regression (GPR) and SVR, to predict the ship speed by using publicly available sensor data from a single domestic ferry, the \"M/S Smyril\", operating around the Faroe Islands. Similarly, ref. [30] investigate deep learning models for predicting a rise in the speed loss caused by marine fouling, obtaining higher accuracies compared to the ISO 19030. In this study, the authors used real-world data coming from two Handymax chemical/product tankers for a year and a half. Although emissions are crucial for a sustainable shipping industry, especially with the IMO's initiative of the International Convention for the Prevention of Pollution from Ships, there has been less focus on direct emissions' prediction.