A machine learning framework to forecast wave conditions

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

We have designed, built, tested and fielded a decision support system which provides a platform for situational awareness, planning, observation, archiving and data analysis. While still in development, our inter-disciplinary team of computer scientists, engineers, biologists and oceanographers has made extensive use of our system in at-sea experiments since 2010. The novelty of our work lies in the targeted domain, its evolving functionalities that closely tracks how ocean scientists are seeing the evolution of their own work practice, and its actual use by engineers, scientists and marine operations personnel. We describe the architectural elements and lessons learned over the more than two years use of the system.

https://robotics.usc.edu/publications/downloads/pub/790/

ODSS: A decision support system for ocean exploration

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.

A Machine Learning Framework to Forecast Wave Conditions

This study investigates near-shore circulation and wave characteristics applied to a case-study site in Monterey Bay, California. We integrate physics-based models to resolve wave conditions (based on inputs from a global wave model, wind data from an operational weather platform, and predictions from a regional flow model) together with a linear machine learning algorithm that combines forecasts from multiple, independent models into a single “best-estimate” prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind-augmented waves in coastal and inland waters. Wave-condition data reported every 30 minutes were gathered from the National Oceanic and Atmospheric Administrations National Data Buoy Center. These data permit evaluation of the fundamental model performance, training of the machine-learning algorithm, and assessment of the ability of the integrated system to make predictions. Ensembles are developed based on multiple simulations perturbing input data to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. Finally, we compare the weighted ensemble predictions (forecasts) with measured data to evaluate performance against current state-of-the-art.

An ensemble-based approach that combines machine learning and numerical models to improve forecasts of wave conditions

The U.S. Integrated Ocean Observing System (U.S. IOOS®) is a network of eleven regional ocean observing systems coordinating observations of the nation's oceans, coasts, and Great Lakes. Three of these regional systems span the continental U.S. West Coast: the Southern California Coastal Ocean Observing System (SCCOOS), the Central and Northern California Ocean Observing System (CeNCOOS), and the Northwest Association of Networked Ocean Observing Systems (NANOOS) (Figure 1). Each regional coastal ocean observing system (RCOOS) provides key observations, data, and information products that meet unique place-based needs through collaborations between academic institutions, state, tribal and federal agencies, private industry, and non-profit organizations.

http://www.cencoos.org/sites/default/files/documents/Oceans_2012_West_Coast_IOOS_FINAL_smaller.pdf

Addressing ocean and coastal issues at the West Coast scale through regional ocean observing system collaboration

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Addressing ocean and coastal issues at the West Coast scale through regional ocean observing system collaboration