文献类型：会议论文

英文题名：Significant Wave Height Prediction Based on MSFD Neural Network

作者：Wang, Huan[1,2];Fu, Dongyang[2,3];Liao, Shan[2,3];Wang, Guancheng[2,3];Xiao, Xiuchun[2,3]

机构：[1]Guangdong Ocean Univ, Sch Oceanog & Meteorol, Zhanjiang, Peoples R China;[2]Guangdong Ocean Univ, Shenzhen Inst, Shenzhen, Peoples R China;[3]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang, Peoples R China

会议论文集：10th International Conference on Intelligent Control and Information Processing (ICICIP)

会议日期：DEC 14-19, 2019

会议地点：Marrakesh, MOROCCO

语种：英文

外文关键词：significant wave height (SWH); multiple sine functions decomposition (MSFD); neural networks; prediction algorithm; South China Sea

外文摘要：Due to the complicated behavior of the ocean wave, significant wave height (SWH) prediction is a difficult field in physical oceanography. In this paper, a novel neural network model, based on multiple sine functions decomposition (MSFD), is exploited to achieve the prediction of SWH. Different from traditional models built on physical processes of wave generation and dissipation, the method presented in this paper predicts and analyzes SWH from a mathematical statistical perspective. In particular, the variation rules of the SWH are learned by decomposing the mapping from time to SWH into a plurality of sine functions, and then the new data are predicted by linear combination of these sine functions. Correlation analysis and error between the forecast data and the actual data indicate that the MSFD neural network performs well in predicting SWH data.