A real-time spatiotemporal machine learning framework for the prediction of nearshore wave conditions
Chen, J; Ashton, IGC; Steele, ECC; et al.Pillai, AC
Date: 14 March 2023
Article
Journal
Artificial Intelligence for the Earth Systems
Publisher
American Meteorological Society
Publisher DOI
Abstract
The safe and successful operation of offshore infrastructure relies on a detailed awareness of ocean wave conditions. Ongoing growth in offshore wind energy is focused on very large scale projects, deployed in ever-more challenging environments. This inherently increases both cost and complexity, and therefore the requirement for ...
The safe and successful operation of offshore infrastructure relies on a detailed awareness of ocean wave conditions. Ongoing growth in offshore wind energy is focused on very large scale projects, deployed in ever-more challenging environments. This inherently increases both cost and complexity, and therefore the requirement for efficient operational planning. To support this, we propose a new machine learning framework for the short-term forecasting of ocean wave conditions, to support critical decision-making associated with marine operations. Here, an attention-based Long Short-Term Memory (LSTM) neural network approach is used to learn the short-term temporal patterns from in-situ observations. This is then integrated with an existing, low-computational cost spatial nowcasting model to develop a complete framework for spatio-temporal forecasting. The framework addresses the challenge of filling gaps in the in-situ observations, and undertakes feature selection, with seasonal training datasets embedded. The full spatio-temporal forecasting system is demonstrated using a case study based on independent observation locations near the southwest coast of the United Kingdom. Results are validated against in-situ data from two wave buoy locations within the domain and compared to operational physics-based wave forecasts from the Met Office (the UK’s national weather service). For these two example locations, the spatio-temporal forecast is found to have the accuracy of R2 0.9083 and 0.7409 in forecasting 1 hour ahead significant wave height, and R2 0.8581 and 0.6978 in 12 hour ahead forecasts, respectively. Importantly, this represents respectable levels of accuracy, comparable to traditional physics-based forecast products, but requires only a fraction of the computational resources.
Engineering
Faculty of Environment, Science and Economy
Item views 0
Full item downloads 0
Except where otherwise noted, this item's licence is described as © 2023 American Meteorological Society. This article is licensed under a Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0/).