首页|Wave height forecast method with uncertainty quantification based on Gaussian process regression
Wave height forecast method with uncertainty quantification based on Gaussian process regression
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
Wave height forecast method with uncertainty quantification based on Gaussian process regression
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.
Wave height forecastdata-driven modelingGaussian process regression(GPR)bayes inferencecovariance function
Zi-lu Ouyang、Chao-fan Li、Ke Zhan、Chuan-qing Li、Ren-chuan Zhu、Zao-jian Zou
展开 >
School of Ocean and Civil Engineering,Shanghai Jiao Tong University,Shanghai 200240,China
State Key Laboratory of Navigation and Safety Technology,Shanghai Ship and Shipping Research Institute CO.,Ltd,Shanghai 200135,China
State Key Laboratory of Ocean Engineering,Shanghai Jiao Tong University,Shanghai 200240,China
Wave height forecast data-driven modeling Gaussian process regression(GPR) bayes inference covariance function
NETL
NSTL
万方数据
