A Machine Learning Framework to Forecast Wave Conditions

TL;DR

This paper presents a machine learning framework trained on physics-based wave model data to accurately forecast ocean wave heights and periods with significantly reduced computational cost, demonstrated in Monterey Bay.

Contribution

It introduces a novel supervised learning approach that efficiently predicts wave conditions, reducing computation time by over 99% compared to traditional physics-based models.

Findings

Root-mean-squared error of 9cm for wave height prediction

Over 90% accuracy in identifying characteristic wave periods

Forecasting computation time is less than 0.1% of physics-based models

Abstract

A~machine learning framework is developed to estimate ocean-wave conditions. By supervised training of machine learning models on many thousands of iterations of a physics-based wave model, accurate representations of significant wave heights and period can be used to predict ocean conditions. A model of Monterey Bay was used as the example test site; it was forced by measured wave conditions, ocean-current nowcasts, and reported winds. These input data along with model outputs of spatially variable wave heights and characteristic period were aggregated into supervised learning training and test data sets, which were supplied to machine learning models. These machine learning models replicated wave heights with a root-mean-squared error of 9cm and correctly identify over 90% of the characteristic periods for the test-data sets. Impressively, transforming model inputs to outputs through matrix operations requires only a fraction (<1/1,000) of the computation time compared to forecasting with the physics-based model.