Ensemble model aggregation using a computationally lightweight machine-learning model to forecast ocean waves

Abstract

This study investigated an approach to improve the accuracy of computationally lightweight surrogate models by updating forecasts based on historical accuracy relative to sparse observation data. Using a lightweight, ocean-wave forecasting model, we created a large number of model ensembles, with perturbed inputs, for a two-year study period. Forecasts were aggregated using a machine-learning algorithm that combined forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The framework was applied to a case-study site in Monterey Bay, California. A~learning-aggregation technique used historical observations and model forecasts to calculate a weight for each ensemble member. Weighted ensemble predictions were compared to measured wave conditions to evaluate performance against present state-of-the-art. Finally, we discussed how this framework, which integrates ensemble aggregations and surrogate models, can be used to improve forecasting systems and further enable scientific process studies.

References in corpus (1)

• Enabling large-scale viscoelastic calculations via neural network acceleration

Cited by in corpus (5)

• Statistical and machine learning ensemble modelling to forecast sea surface temperature
• Using Deep Learning to Extend the Range of Air-Pollution Monitoring and Forecasting
• Artificial neural networks ensemble methodology to predict significant wave height
• A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing
• REBEC: Robust Evolutionary-based Calibration Approach for the Numerical Wind Wave Model