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This content will become publicly available on May 30, 2025

Title: Forecasting Ocean Waves off the U.S. East Coast Using an Ensemble Learning Approach
Abstract

This study introduces an ensemble learning model for the prediction of significant wave height and average wave period in stations along the U.S. Atlantic coast. The model utilizes the stacking method, combining three base learner models - Lasso regression, support vector machine, and Multi-layer Perceptron - to achieve more precise and robust predictions. To train and evaluate the models, a twenty-year dataset comprising meteorological and wave data was used, enabling forecasts for significant wave height and average wave period at 1, 3, 6, and 12 hour intervals. The data collection involved two NOAA buoy stations situated on the U.S. Atlantic coast. The findings demonstrate that the ensemble learning model constructed through the stacking method yields significantly higher accuracy in predicting significant wave height within the specified time intervals.

Moreover, the study investigates the influence of swell waves on forecasting significant wave height and average wave period. Notably, the inclusion of swell waves improves the accuracy of the 12-hour forecast. Consequently, the developed ensemble model effectively estimates both significant wave height and average wave period. The ensemble model outperforms the individual models in forecasting significant wave height and average wave period. This ensemble learning model serves as a viable alternative to conventional coastal models for predicting wave parameters.

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Award ID(s):
2019758 2223844
PAR ID:
10512627
Author(s) / Creator(s):
; ;
Publisher / Repository:
AMS Journals
Date Published:
Journal Name:
Artificial Intelligence for the Earth Systems
ISSN:
2769-7525
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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