Multi-Step-Ahead Forecasting of Wave Conditions Based on a Physics-Based Machine Learning (PBML) Model for Marine Operations

• Main Authors: Mengning Wu, Christos Stefanakos, Zhen Gao
• Format: Article
• Language: English
• Published: MDPI AG 2020-12-01
• Series: Journal of Marine Science and Engineering
• Subjects: wave forecasting; machine learning modeling; marine operations; uncertainty quantification
• Online Access: https://www.mdpi.com/2077-1312/8/12/992

Short-term wave forecasts are essential for the execution of marine operations. In this paper, an efficient and reliable physics-based machine learning (PBML) model is proposed to realize the multi-step-ahead forecasting of wave conditions (e.g., significant wave height <i>H_s</i> and peak wave period <i>T_p</i>). In the model, the primary variables in physics-based wave models (i.e., the wind forcing and initial wave boundary condition) are considered as inputs. Meanwhile, a machine learning algorithm (artificial neural network, ANN) is adopted to build an implicit relation between inputs and forecasted outputs of wave conditions. The computational cost of this data-driven model is obviously much lower than that of the differential-equation based physical model. A ten-year (from 2001 to 2010) dataset of every three hours at the North Sea center was used to assess the model performance in a small domain. The result reveals high reliability for one-day-ahead <i>H_s</i> forecasts, while that of <i>T_p</i> is slightly lower due to the weaker implicit relationships between the data. Overall, the PBML model can be conceived as an efficient tool for the multi-step-ahead forecasting of wave conditions, and thus has great potential for furthering assist decision-making during the execution of marine operations.