Validation of Wave Forecasting with the Sverdrup, Munk, and Bretschneider (SMB) Method Using Easywave Algorithm

Ghifari Raihan Silam Siregar, Husein Alfarizi, Florence Mila Purnomo, Satria Ginanjar, A. Wirasatriya
{"title":"Validation of Wave Forecasting with the Sverdrup, Munk, and Bretschneider (SMB) Method Using Easywave Algorithm","authors":"Ghifari Raihan Silam Siregar, Husein Alfarizi, Florence Mila Purnomo, Satria Ginanjar, A. Wirasatriya","doi":"10.1109/AGERS51788.2020.9452769","DOIUrl":null,"url":null,"abstract":"Wave information is crucial for maritime activities such as marine transportation, offshore exploration, fisheries, safety management system on ships, coastal development, and coastal disaster mitigation. The Sverdrup, Munk, Bretschneider. SMB is one of the most common methods used for wave forecasting. In the present study, we examine the validation of Easywave, an algorithm that automates wave forecasting with the SMB method. The results are validated using observational data and analyzed using Mean Relative Error (MRE), Root Mean Square Error (RMSE), and a bias value. The level of accuracy of forecasting using the Easywave algorithm is 89.87% for Hs and 78.43% for Ts. The level of precision obtained by on-field observation data is $\\pm \\mathbf{0.14\\ m}$ for Hs and $\\pm\\mathbf{0.26\\ s}$ for Ts.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"51 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AGERS51788.2020.9452769","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Wave information is crucial for maritime activities such as marine transportation, offshore exploration, fisheries, safety management system on ships, coastal development, and coastal disaster mitigation. The Sverdrup, Munk, Bretschneider. SMB is one of the most common methods used for wave forecasting. In the present study, we examine the validation of Easywave, an algorithm that automates wave forecasting with the SMB method. The results are validated using observational data and analyzed using Mean Relative Error (MRE), Root Mean Square Error (RMSE), and a bias value. The level of accuracy of forecasting using the Easywave algorithm is 89.87% for Hs and 78.43% for Ts. The level of precision obtained by on-field observation data is $\pm \mathbf{0.14\ m}$ for Hs and $\pm\mathbf{0.26\ s}$ for Ts.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
使用Easywave算法验证Sverdrup, Munk, and Bretschneider (SMB)方法的波浪预测
波浪信息对海洋运输、近海勘探、渔业、船舶安全管理系统、沿海开发和沿海减灾等海上活动至关重要。Sverdrup, Munk, Bretschneider。SMB是波浪预报中最常用的方法之一。在本研究中,我们检验了Easywave的有效性,这是一种用SMB方法自动预测波浪的算法。使用观测数据验证结果,并使用平均相对误差(MRE)、均方根误差(RMSE)和偏倚值进行分析。使用Easywave算法预测Hs的精度为89.87%,Ts的精度为78.43%,现场观测数据的精度为$\pm\mathbf{0.14\ m}$, Ts的精度为$\pm\mathbf{0.26\ s}$。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Seasonal Analysis of the Hotspot Spatial Grid in Indonesia and the Relationship of the Hotspot Grid with the Nino SST Indices Radiometric Enhancement of Landsat 8 OLI Imagery Using Coastal/Aerosol Band Development of near real-time and archival Tsunami data visualization dashboard for Indonesia Flood Monitoring with Information Extraction Approach from Social Media Data Data Interoperability and Repository for Oceanography Research
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1