## 1 Introduction
### Literature Review
The authors in [31] developed an ANN to predict emissions from harbour vessels, significantly improving accuracy compared to the bottom-up method introduced by the 4 th IMO GHG Study, especially when meteorological factors are taken into account. The proposed method used data from a single harbour vessel operating within the Singapore port water area over a six-day period. Similarly, less research topics focus on path planning and event detection. In [32], authors used the A* algorithm to optimise path planing, reducing computational time while ensuring safety real-time performance. In [33], an ANN was developed to predict a vessel's future behaviour, such as position, speed and course, based on events that occur in a predictable pattern across large map areas. Finally, ship monitoring has been the focus of several studies. In [34], a prognostics model for electric motors was developed, combining data-driven operational models and physics-informed degradation models to predict motor degradation. For the operational models, the authors explored Gradient Tree Boosting Machine (GTBM), RF, ANN and Linear Regression (LR) using environmental variables and ship speed parameters. Similarly,in [35], Ridge and LASSO regularisation methods were presented to model the performance monitoring problem with data from a single ferry vessel. SVMs were employed in [36] for classification to detect shaft misalignment. Finally, in [37], the authors investigated various machine learning models--such as RF, KNN, ET, GTBM, LR and SVM--for ship trim optimisation by utilising data from Internet of Things (IoT) sensors. Overall, machine learning techniques have been applied to a wide range of maritime challenges, from predicting vessel behaviour and fuel consumption to optimising trim and propulsion power. Despite their extensive use, many studies have notable limitations: some focus on specific vessel types, while others rely on data from a single vessel or limited routes and time periods. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Research Topic** & **Methods** & \begin{tabular}{l} **Number of** \\ **Vessels** \\ \end{tabular} & \begin{tabular}{l} **Dataset** \\ **Timespan** \\ \end{tabular} & **Vessel Types** & **Citation** \\ \hline \multirow{7}{*}{**Power Prediction**} & NL-PCR, NL-PLSR, ANN & 2 & & & [6] \\ \cline{2-6}  & XGBoost, ANN, SVR & & & \begin{tabular}{l} Chemical tanker, \\ PCTC vessel \\ \end{tabular} & [7] \\ \cline{2-6}  & LR, DT, KNN, ANN, RF & 5 & & \begin{tabular}{l} Container ships \\ (8700 TEU capacity) \\ \end{tabular} & [8] \\ \cline{2-6}  & SVR & 1 & 7 months & \begin{tabular}{l} Bulk cargo ship \\ (200,000 tons) \\ \end{tabular} & [9] \\ \cline{2-6}  & ANN & 2 & 8 years & Car-carrying vessels & [10] \\ \cline{2-6}  & LR & & & [11] \\ \cline{2-6}  & ENN, ANN & & & [12] \\ \cline{2-6}  & ANN & & & [13] \\ \cline{2-6}  & MLR, DT, KNN, ANN, RF & & & [8] \\ \cline{2-6}  & RNN, CNN & & 719 days & [14] \\ \cline{2-6}  & MLP, CNN & & & [16] \\ \cline{2-6}  & RF & 16 months & general cargo ship & [15] \\ \hline \multirow{7}{*}{**Fuel Consumption**} & White, Black, Gray & 1 & \begin{tabular}{l} Handymax \\ chemical/product tanker \\ \end{tabular} & [17] \\ \cline{2-6}  & Box Models & 1 & \begin{tabular}{l} Pandymax \\ chemical/product tanker \\ \end{tabular} & [18] \\ \cline{2-6}  & ANN & 1 & \begin{tabular}{l} Pogoria ship \\ \end{tabular} & [19] \\ \cline{2-6}  & LASSO & 97 & 3.5 years & Container ships & [19] \\ \cline{2-6}  & MLR & & \
\begin{tabular}{l} bulk carriers \\ \end{tabular} & [20] \\ \cline{2-6}  & HR, LGBM & & & [21] \\ \cline{2-6}  & MLR, RR, LASSO, SVR & & & [22] \\ \cline{2-6}  & ANN, GPR & & & [23] \\ \cline{2-6}  & ANN, MLR & 6 months &
\begin{tabular}{l} 13,000 TEU \\ class container \\ \end{tabular} & [24] \\ \cline{2-6}  & ANN & & & [25] \\ \cline{2-6}  & SVR, RF, ET, ANN & & [26] \\ \hline
**Resistance Prediction** & RT, SVR, ANN & Three types of Sailboats & [27] \\ \cline{2-6}  & ANN & & [28] \\ \hline \hline \end{tabular}
\end{table}