A Real-Time Spatiotemporal 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.

Revolutionizing Computational Efficiency: A Comprehensive Analysis of Virtual Machine Optimization Strategies

“…Furthermore, recent investigations by Kim and Patel (2022) delved into the integration of machine learning algorithms for predictive resource provisioning in virtualized systems. The intersection of artificial intelligence and VM optimization is a burgeoning field, as evidenced by the work of Chen et al (2023), who proposed a self-learning VM management framework that adapts to workload variations in real-time.…”