Understanding the Dynamics of Ocean Wave-Current Interactions Through Multivariate Multi-Step Time Series Forecasting

Understanding ocean wave-current interactions' complex dynamics is crucial for coastal engineering, marine operations, and climate research applications. This study introduces a pioneering data-driven approach by employing advanced deep learning techniques, specifically Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) models, to forecast both wave and current parameters at varying depths. The models are designed to capture the complex temporal relationships inherent in ocean dynamics, considering wave speed and direction, current speed, and direction as multivariate time series inputs. Two comprehensive experiments are conducted, one utilizing historical values of all parameters and another focusing on using wave parameters to forecast current parameters. Model performance is rigorously evaluated across forecast horizons of 5, 12, and 24 hours ahead using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). BiLSTM emerges as the superior model, demonstrating lower errors, particularly at higher depths, while nearshore predictions reveal challenges in shallower waters. Furthermore, the methodology incorporates hyperparameter optimization and cross-validation techniques to enhance the model's robustness. Ultimately, this work represents a transformative leap toward smarter oceans, emphasizing the fusion of fluid dynamics and bathymetry to advance our understanding of coupled wave-current dynamics. The results showcase high accuracy and reliability across various forecast horizons and depths, signifying the method's potential applications in oceanography, hydrodynamics, coastal engineering, and ocean renewable energy.