Marvelocity Pdf ~upd~ Official

\subsection{Limitations} \begin{itemize} \item \textbf{Data sparsity in polar regions}: AIS coverage is lower, leading to higher uncertainties. \item \textbf{Propeller efficiency assumption}: We treat $\eta_p$ as a constant; future work will embed a learnable efficiency model. \item \textbf{Real‑time constraints}: While inference is sub‑millisecond, integrating high‑resolution forecasts (e.g., ECMWF) adds latency; edge‑computing strategies are under investigation. \end{itemize}

\section{Conclusion} \label{sec:conclusion} We presented **MarVelocity**, a hybrid metric that blends classical hydrodynamic resistance modelling with a universal machine‑ marvelocity pdf

\section{Results} \label{sec:results} \subsection{Prediction Accuracy} Table~\ref{tab:accuracy} summarizes error metrics on the held‑out test fleet (150 vessels, 1.1 M observations). and current speed

\begin{figure}[H] \centering \includegraphics[width=0.75\linewidth]{ablation.png} \caption{Ablation results: MAE increase when a feature group is omitted.} \label{fig:ablation} \end{figure} aligning with maritime operational experience.

\subsection{Learning the Residual} Define the residual speed: \begin{equation} \Delta V = V_{\text{SOG}} - V_{\text{HM}}, \end{equation} where $V_{\text{SOG}}$ is the measured speed over ground from AIS. We train a Gradient‑Boosted Regression Tree (XGBoost \cite{Chen2016}) to predict $\Delta V$ from the feature vector $\mathbf{x}$: \[ \mathbf{x} = \bigl[\,\underbrace{L, B, D, C_B}_{\text{design}};\, \underbrace{V_{\text{HM}}}_{\text{baseline}};\, \underbrace{U_{10}, \theta_{\text{wind}}}_{\text{wind}};\, \underbrace{H_s, \theta_{\text{wave}}}_{\text{wave}};\, \underbrace{U_c, \theta_{\text{current}}}_{\text{current}}\,\bigr]. \]

\section{Discussion} \label{sec:discussion} \subsection{Interpretability} Feature importance (gain) indicates that $V_{\text{HM}}$ accounts for 38 \% of the model’s predictive power, confirming that the physics‑based backbone remains dominant. The top three environmental variables are wind speed, wave height, and current speed, aligning with maritime operational experience.