Automated Machine Learning-Based Significant Wave Height Prediction for Marine Operations

Aug 4, 2026

Significant wave height (Hs) is a pivotal parameter governing marine load and drives decisions such as route planning and offshore installation. This work addresses persistent obstacles in data-driven Hs prediction, including hyperparameter tuning complexity and weak spatial generalization of conventional machine learning models. Multiple AutoML frameworks are evaluated on buoy-based forecasting tasks to compare accuracy and robustness across forecast horizons and data-quality conditions. Results show PyCaret excels in short-term prediction while AutoGluon improves medium- and long-term performance. A multi-point data fusion approach with PCA is further introduced to enable cross-station forecasting using nearby stations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages21

Reading Time53 min

AccessFree to download

Abstract

Introduction

Article Automated Machine Learning-Based Significant Wave Height Prediction for Marine Operations Yuan Zhang 1, Hao Wang 1, *, Bo Wu 1, Jiajing Sun 1, Mingli Fan 1, Shu Dai 2, Hengyi Yang 3,* and Minyi Xu 1 Academic Editor: Eugen Rusu Received: 2 July 2025 Revised: 27 July 2025 Accepted: 30 July 2025 Published: 31 July 2025 Citation: Zhang, Y.; Wang, H.; Wu, B.; Sun, J.; Fan, M.; Dai, S.; Yang, H.; Xu, M. Automated Machine Learning-Based Significant Wave Height Prediction for Marine Operations. J. Mar. Sci. Eng. 2025, 13, 1476. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/jmse13081476](10.3390/jmse13081476) Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) . 1 Marine Engineering College, Dalian Maritime University, Dalian 116026, China 2 Shanghai Investigation, Design & Research Institute Co., Ltd., Shanghai 200335, China 3 School of Engineering, Newcastle University, Newcastle NE1 7RU, UK * [Correspondence: hao8901@dlmu.edu.cn](Correspondence: hao8901@dlmu.edu.cn) (H.W.); [h.yang39@newcastle.ac.uk](h.yang39@newcastle.ac.uk) (H.Y.) Abstract Determining/predicting the environment dominates a variety of marine operations, such as route planning and offshore installation. Significant wave height (Hs) is a critical parameterdefining wave, a dominating marine load. Data-driven machine learning methods have been increasingly applied to Hs prediction, but challenges remain in hyperparameter tuning and spatial generalization. This study explores a novel effective approach for intelligent Hs forecasting for marine operations. Multiple automated machine learning (AutoML) frameworks, namely H2O, PyCaret, AutoGluon, and TPOT, have been systematically evaluated on buoy-based Hs prediction tasks, which reveal their advantages and limitations under various forecast horizons and data quality scenarios. The results indicate that PyCaret achieves superior accuracy in short-term forecasts, while AutoGluon demonstrates better robustness in medium-term and long-term predictions. To address the limitations of single-point prediction models, which often exhibit high dependence on localized data and limited spatial generalization, a multi-point data fusion framework incorporating Principal Component Analysis (PCA) is proposed. The framework utilizes Hs data from two stations near the California coast to predict Hs at another adjacent station. The results indicate that it is possible to realize cross-station predictions based on the data from adjacent (high relevance) stations. Keywords: significant wave height; automated machine learning; data fusion; spatial generalization 1. Introduction A wave is one of the most important marine dynamic loads [1] . Significant wave height (Hs) is the core parameter-defining wave and is of key significance for the safety of floating structures and marine operations [2] . With the rapid development of offshore oil and gas, renewable energy (e.g., wind and wave), and marine transportation, demands on Hs prediction for the industries are growing fast [3] . Hs predictions are of great value for marine operation decision-making (e.g., window selection) and ship route optimization. Currently, Hs prediction can be divided into numerical simulation based on physical formulations and data-driven machine learning methods. Wave models based on physical equations are the traditional method for wave height prediction [4] . These models simulate the temporal and spatial evolution of ocean waves based on meteorological wind fields, ocean dynamics equations, and source–sink term parameterizations [5] . They are grounded in solid theoretical foundations and capable of characterizing complex p

FAQs

Why is significant wave height (Hs) important for marine operations?

Hs is a core parameter that defines wave conditions and strongly influences the safety of floating structures and the reliability of marine operation decisions such as route planning and window selection.

What limitations do traditional ML/DL methods face for Hs prediction?

They often require extensive hyperparameter tuning and can struggle with feature engineering, model architecture selection, and limited spatial generalization across different sea areas and observation scales.

How do the evaluated AutoML frameworks differ in forecasting performance?

PyCaret achieves superior accuracy for short-term forecasts, whereas AutoGluon demonstrates better robustness for medium-term and long-term predictions under varying data-quality scenarios.

Aug 4, 2026

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