A machine learning framework to forecast wave conditions

By Scott C. James, Yushan Zhang, Fearghal O'Donncha. Coastal Engineering, 2017

344 citations recorded by the source corpus.

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.

Subfields: Environmental Engineering, Oceanography

View Paper: https://www.semanticscholar.org/paper/4a213f73aa80457911f8434d6824f7ac9c0096b4

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Citations in the corpus

Graph Element Networks: adaptive, structured computation and memory (2019, methodology): “Others, such as James et al. (2018) have used data from a PDE solver to train a neural network.”