## 2 Methods and Materials  
### Dataset Description  
In the era of digital transformation in the shipping industry, the wide use of sensors and IoT devices--throughout the whole sector and primarily onboard the fleet providing real-time data--offers the opportunity to monitor the infrastructure on vessels, reduce both fuel consumption and emissions, and optimise operations and safety overall. This research utilises a dataset collected by the _GIS Vessels Monitoring Platform_[38, 39, 40] that has been developed and deployed at the Angelicoussis group ([[https://angelicoussisgroup.com/](https://angelicoussisgroup.com/)]([https://angelicoussisgroup.com/](https://angelicoussisgroup.com/))(accessed on 1 June 2025)). The platform gathers heterogeneous data from numerous sensors and IoT devices from the vessels, processes them properly, enriches them with data from external \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{3}{*}{\begin{tabular}{l} **Speed** \\ **Prediction** \\ \end{tabular} } & LR, RT GP, SVR & 1 & \begin{tabular}{l} Domestic ferry \\ (“M/Smyril”) \\ \end{tabular} & [29] \\ \cline{2-6}  & Deep Learning models & 2 & 1.5 years & \begin{tabular}{l} Handymax \\ chemical/product tankers \\ \end{tabular} & [30] \\ \hline \begin{tabular}{l} **Emissions’** \\ **Prediction** \\ \end{tabular} & ANN & 1 & 6 days & \begin{tabular}{l} Harbour vessel \\ \end{tabular} & [31] \\ \hline \begin{tabular}{l} **Path Planning** \\ \end{tabular} & A* Algorithm & & & [32] \\ \hline \
\begin{tabular}{l} **Event Detection** \\ \end{tabular} & ANN & & & [33] \\ \hline \multirow{4}{*}{
\begin{tabular}{l} **Monitoring** \\ \end{tabular} } & GB, RF, ANN, LR & & & [34] \\ \cline{2-6}  & RF, KNN, ET, GBM, & & Various vessels & [37] \\ \cline{1-1} \cline{2-6}  & LR, SVM & & & \\ \cline{1-1} \cline{2-6}  & Ridge, LASSO & 1 & Ferry vessel & [35] \\ \cline{1-1} \cline{2-6}  & SVM & & & [36] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._APIs, such as weather conditions, and presents them in a single information reference point supporting numerous end applications for monitoring and decision making. Our dataset contains 237K data points gathered from a group of 9 sister VLCC tankers for 36 months (from March 2020 to March 2023). The dataset consists of 12 input features, which cover diverse factors regarding the operational and environmental conditions during the voyage, and one target variable, the shaft power generated by the ship's engine. All features are shown in Table 2, along with their unit of measurement. In more detail, the _GPS Speed_ denotes the ship's speed over ground as measured by GPS and is a key indicator of the ship's movement. The _Draft_ measures the depth of the hull below the waterline and effectively determines the vessel's buoyancy and stability. The _Days from Dry Dock_ and the _Days from Delivery_ represent the period since the last dry dock of the ship and delivery, respectively, providing crucial information regarding its maintenance history and age. All these features cover the operational conditions during a voyage. Regarding the environmental conditions, there are features for waves, wind, current and sea characteristics. Specifically, the _Wave Height_ indicates the height of waves encountered, reflecting the sea conditions and potential challenges in rough waters. The _Wave Relative Direction_ describes the direction from which waves approach the ship to its orientation and enables us to convey wave interactions and their impact on vessel dynamics. The _Wind Speed_ indicates the speed of wind experienced by the ship, which can influence the efficiency of propulsion and its ability to manoeuvre.