基于SVM的海浪要素预测试验研究

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英文篇名：Prediction of the Significant Wave Height Based on the Support Vector Machine 作者：金权 ; 华锋 ; 杨永增 英文作者：JIN Quan;HUA Feng;YANG Yong-zeng;First Institute of Oceanography,MNR; 关键词：支持向量机 ; 海浪要素预测 ; 海浪数值模式 英文关键词：Support Vector Machine;;significant wave height;;wave numerical model 中文刊名：海洋科学进展 英文刊名：Advances in Marine Science 机构：自然资源部第一海洋研究所; 出版日期：2019-04-15 出版单位：海洋科学进展 年：2019 期：02 基金：国家重点研究发展计划项目——大气海洋耦合机制、同化方法与数值模式研究(2017YFC1404200) 语种：中文; 页：43-53 页数：11 CN：37-1387/P ISSN：1671-6647 分类号：P731.22

摘要

采用支持向量机对海浪要素中的有效波高进行预测,采用风场和波浪场作为学习要素,对比不同特征向量对有效波高预测结果的准确度。取台湾岛东部海区作为实验区域,使用NCEP再分析的数值模式数据作为学习样本。选用支持向量分类机,建立了4组不同特征向量的模型进行海浪有效波高的预测,并对4种模型的结果进行比较和分析。实验表明,当输入的特征向量过多或过少时,会对模型的预测结果和计算效率产生不同的影响。当使用风场和波浪场共同作为特征向量进行学习时,在该区域预测结果与模式预报结果相比更接近,相关系数将近99%,均方根误差约0.2 m。
        Support vector machine(SVM) is used to predict the significant wave height, in which wind field and wave field are adopted as learning parameters, and the influence of different eigenvectors on the prediction is analyzed. The domain of the SVM is located to the southeast of the Taiwan Island and the NCEP reanalysis data are used as learning samples. By using support vector classification, we built 4 models with different feature vectors and predicted the significant wave height. Results show that feature vectors can impact the accuracy and computation speed. When wind field and wave field are adopted as eigenvectors for learning, the correlation coefficient is nearly 99% and the root mean square error is about 0.2 m in comparison with numerical model simulations.

引文

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