\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).
\section{Conclusion} \label{sec:conclusion} We presented **MarVelocity**, a hybrid metric that blends classical hydrodynamic resistance modelling with a universal machine‑ marvelocity pdf
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} \end{itemize} Model training is performed on a single
\bigskip \noindent\textbf{Keywords:} maritime speed prediction, AIS data, hydrodynamic resistance, machine learning, fuel efficiency, autonomous vessels The top three environmental variables are wind speed,
\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.
\subsection{Future Work} \begin{enumerate} \item Extension to **fuel‑consumption** prediction via a joint multi‑task network. \item Incorporation of **ship‑maneuvering** dynamics for autonomous docking. \item Open‑source **benchmark suite** for maritime speed prediction (datasets, evaluation scripts). \end{enumerate}