基于AR-SVR模型的有效波高短期预测  

SHORT-TERM PREDICTION OF SIGNIFICANT WAVE HEIGHT BASED ON AR-SVR MODEL

作　　者：张振全 李醒飞[1,2,3] 杨少波 Zhang Zhenquan;Li Xingfei;Yang Shaobo(State Key Laboratory of Precision Measuring Technology and Instruments,Tianjin University,Tianjin 300072,China;Qingdao Institute for Ocean Technology of Tianjin University,Qingdao 266200,China;Pilot National Laboratory for Marine Science and Technology(Qingdao),Qingdao 266003,China)

机构地区：[1]天津大学精密测试技术及仪器国家重点实验室,天津300072 [2]天津大学青岛海洋技术研究院,青岛266200 [3]青岛海洋科学与技术试点国家实验室,青岛266003

出　　处：《太阳能学报》2021年第7期15-20,共6页Acta Energiae Solaris Sinica

基　　金：青年教师科研启动基金(Pilq1702);青岛海洋科学与技术试点国家实验室“问海计划”专项(ZR2016WH01)。

摘　　要：为快速、准确地预测波浪的有效波高,该文提出了一种基于平均交互信息(AMI)特征选择的自回归(AR)模型与支持向量回归(SVR)混合的短期有效波高预测算法。AR-SVR模型结合了有效波高序列本身的统计特性,同时考虑到驱动风场的影响。该文比较了AR-SVR模型与AR、SVR模型的预测性能,预测结果表明,AR-SVR混合模型预测结果优于单一的AR和SVR模型。In order to predict the significant wave height accurately and quickly,our methodology utilizes an autoregressive modelsupport vector regression algorithm(AR-SVR)based on the average mutual information for feature selection.The AR-SVR model combines the statistical characteristics of the significant wave height series and considers the influence of driving wind field.This paper compares the prediction performance of AR-SVR model with AR and SVR model,and the performance study results demonstrate that AR-SVR performs better than the AR and SVR with higher prediction accuracy.

关 键 词：波浪能 时间序列 支持向量机 海浪 AR-SVR 预测 

分 类 号：P743.2[天文地球—海洋科学]