.png){kind=icon}
.png){kind=icon}
Return
Wave height forecast method with uncertainty quantification based on Gaussian process regression
.png){kind=icon}
PRE
AI
DOI：10.1007/s42241-024-0070-2.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Wave height forecast (WHF) is of great significance to exploit the marine renewables and improve the safety of ship navigation at sea. With the development of machine learning technology, WHF can be realized in an easy-to-operate and reliable way, which improves its engineering practicability. This paper utilizes a data-driven method, Gaussian process regression (GPR), to model and predict the wave height on the basis of the input and output data. With the help of Bayes inference, the prediction results contain the uncertainty quantification naturally. The comparative studies are carried out to evaluate the performance of GPR based on the simulation data generated by high-order spectral method and the experimental data collected in the deep-water towing tank at the Shanghai Ship and Shipping Research Institute. The results demonstrate that GPR is able to model and predict the wave height with acceptable accuracy, making it a potential choice for engineering application.
Keywords:
Wave height forecast
data-driven modeling
Gaussian process regression (GPR)
bayes inference
covariance function
Journal
IF:
3.5
Papers:
2.4K
Citations:
4.0K