Journal Article

Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network

Zhou, Shuyi, Dong, Changming, Bethel, Brandon J., Sun, Wenjin, Zhao, Yang, Xie, Wenhong

2021

Overview

Wave forecasts, though integral to ocean engineering activities, are often conducted using computationally expensive and time-consuming numerical models with accuracies that are blunted by numerical-model-inherent limitations. Additionally, artificial neural networks, though significantly computationally cheaper, faster, and effective, also experience difficulties with nonlinearities in the wave generation and evolution processes. To solve both problems, this study employs and couples empirical mode decomposition (EMD) and a long short-term memory (LSTM) network in a joint model for significant wave height forecasting, a method widely used in wind speed forecasting, but not yet for wave heights. Following a comparative analysis, the results demonstrate that EMD-LSTM significantly outperforms LSTM at every forecast horizon (3, 6, 12, 24, 48, and 72 h), considerably improving forecasting accuracy, especially for forecasts exceeding 24 h. Additionally, EMD-LSTM responds faster than LSTM to large waves. An error analysis comparing LSTM and EMD-LSTM demonstrates that LSTM errors are more systematic. This study also identifies that LSTM is not able to adequately predict high-frequency significant wave height intrinsic mode functions, which leaves room for further improvements.

• Publisher: MDPI AG
• Subject: Artificial neural networks / Comparative analysis / Decomposition / EMD-LSTM / Empirical analysis / empirical mode decomposition / Error analysis / Forecasting / Information storage / Long short-term memory / long short-term memory network / Mathematical models / Model accuracy / Neural networks / Numerical models / Ocean engineering / Offshore engineering / Significant wave height / significant wave heights / Support vector machines / Time series / wave forecasting / Wave generation / Wave height / Wind speed