A Real-Time Spatio-Temporal Machine Learning Framework for the Prediction of Nearshore Wave Conditions

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 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.