Deep Wave Prediction (Deep-WP) is a repository containing code to run examples in our paper : Deterministic wave prediction model for irregular long-crested waves with Recurrent Neural Network.
Real-time predicting of stochastic waves is crucial in marine engineering. In this paper, a deep learning wave prediction (Deep-WP) model based on the ‘probabilistic’ strategy is designed for the short-term prediction of stochastic waves. The Deep-WP model employs the long short-term memory (LSTM) unit to collect pertinent information from the wave elevation time series. Five irregular long-crested waves generated in the deepwater offshore basin at Shanghai Jiao Tong University are used to validate and optimize the Deep-WP model. When the prediction duration is 1.92s, 2.56s, and, 3.84s, respectively, the predicted results are almost identical with the ground truth. As the prediction duration is increased to 7.68s or 15.36s, the Deep-WP model’s error increases, but it still maintains a high level of accuracy during the first few seconds. The introduction of covariates will improve the Deep-WP model’s performance, with the absolute position and timestamp being particularly advantageous for wave prediction. Furthermore, the Deep-WP model is applicable to predict waves with different energy components. The proposed Deep-WP model shows a feasible ability to predict nonlinear stochastic waves in real-time.
Figure 1. Schematic diagram of the structure of the Deep-WP model.
Figure 2. Covariates.
If you find this repository useful in your research, please consider citing the following paper:
@article{LIU-DeepWP-2022,
title = {Deterministic wave prediction model for irregular long-crested waves with Recurrent Neural Network},
journal = {Journal of Ocean Engineering and Science},
year = {2022},
issn = {2468-0133},
doi = {https://doi.org/10.1016/j.joes.2022.08.002},
url = {https://www.sciencedirect.com/science/article/pii/S2468013322002340},
author = {Yue Liu and
Xiantao Zhang and
Gang Chen and
Qing Dong and
Xiaoxian Guo and
Xinliang Tian and
Wenyue Lu and
Tao Peng},
}
If you have any questions, feel free to contact Yue Liu through Email ([email protected]) or Github issues. Pull requests are highly welcomed!