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

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.