\section{Methodology} \label{sec:method} \subsection{Data Acquisition} \begin{itemize} \item \textbf{AIS}: 2.3 M messages (2018–2023) from the Global Fishing Watch and MarineTraffic APIs. \item \textbf{Oceanographic Reanalysis}: ERA5 \cite{Hersbach2020} providing 10‑m wind vectors, significant wave height, and surface currents at 0.25° resolution. \item \textbf{Ship Catalog}: Technical specifications (length overall, beam, draft, block coefficient, engine power) extracted from the Lloyd’s Register database. \end{itemize} All timestamps are aligned to UTC and interpolated to a 10‑minute cadence.
\newpage \section{Introduction} \label{sec:intro} The global shipping industry transports over \SI{80}{\percent} of world trade by volume \cite{UNCTAD2022}. Despite advances in hull design and propulsion, a substantial fraction of fuel burn is attributable to sub‑optimal speed choices driven by inaccurate speed forecasts \cite{Mitsui2019}. Conventional approaches—e.g., the Holtrop–Mennen method \cite{Holtrop1972} or the ITTC‑1998 friction line \cite{ITTC1998}—rely on static ship parameters and simplified sea‑state corrections. Such models neglect the complex, nonlinear interaction among wind, waves, currents, and ship trim.
Copy the code into a file named marvelocity.tex , run pdflatex (or your favourite LaTeX engine) and you will obtain a nicely formatted PDF that you can submit to a conference or journal. \documentclass[letterpaper,10pt]{article} \usepackage[margin=1in]{geometry} \usepackage{times} \usepackage{graphicx} \usepackage{amsmath,amssymb} \usepackage{hyperref} \usepackage{booktabs} \usepackage{multirow} \usepackage{siunitx} \usepackage{float} \usepackage{enumitem} \usepackage[backend=biber,style=ieee]{biblatex} \addbibresource{marvelocity.bib} marvelocity pdf
\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.
\begin{table}[H] \centering \caption{Speed prediction errors (knot) across three methods} \label{tab:accuracy} \begin{tabular}{lccc} \toprule Method & MAE & RMSE & $R^{2}$ \\ \midrule Holtrop–Mennen (baseline) & 0.28 & 0.42 & 0.81 \\ XGBoost residual (ship‑specific) & 0.14 & 0.20 & 0.94 \\ \textbf{MarVelocity (universal)} & \textbf{0.12} & \textbf{0.18} & \textbf{0.96} \\ \bottomrule \end{tabular} \end{table} \end{itemize} All timestamps are aligned to UTC and
\subsection{Training Procedure} \begin{itemize} \item \textbf{Train/validation split}: 70 \% ships for training, 15 \% for validation, 15 \% for test (no ship appears in more than one split). \item \textbf{Hyper‑parameter optimisation}: Bayesian optimisation (Optuna \cite{Akiba2019}) over tree depth, learning rate, and number of estimators. \item \textbf{Loss function}: Mean Absolute Error (MAE) on $\Delta V$. \end{itemize} Model training is performed on a single NVIDIA RTX 4090 GPU (≈ 5 min).
The final **MarVelocity** prediction is: \begin{equation} V_{\text{MarV}} = V_{\text{HM}} + \hat{\Delta V}(\mathbf{x}). \end{equation} Conventional approaches—e
\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}