Abstract
Many ocean applications such as control optimization of wave energy converters and ship navigation rely on ocean wave prediction to improve the efficiency and safety of offshore operations. The two main types of ocean wave prediction methods are deterministic sea wave prediction (DSWP) and same point prediction. DSWP are based on several upstream wave measurements, which aid in prediction but are expensive due to the amount of measuring equipment necessary. Furthermore, the majority of DSWP are physical models that work on certain sea states such as deep water condition and are based on a knowledge of ocean physics and the assumptions of linear wave theory. On the other side, same-point prediction solely uses past measurements to estimate future sea elevation, which reduces measurement costs but adds extra complexity. For same-point prediction, researchers in the existing literature apply data-driven models such as auto-regressive models or neural networks; nevertheless, neural networks do not perform better.
In this paper, we examine the performance of Temporal Convolutional Networks (TCNs) and traditional Artificial Neural Networks (ANNs) for short-term ocean wave prediction on the same point wave measurement. With dilated causal convolutional layers and residual blocks, TCNs are designed to extract features from complex long-term patterns and capture the nonlinearity of the wave time series data. Wave time series data are simulated using the Pierson-Moskowitz spectrum with different levels of non-linearity for the experiment. The experiment results illustrate the efficacy of two deep learning models as influenced by nonlinearity and noise levels, but TCNs do not outperform ANNs in the same point wave prediction.
