New Improved Wave Hybrid Models for Short-Term Significant Wave Height Forecasting-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

New Improved Wave Hybrid Models for Short-Term Significant Wave Height Forecasting

Published:2023 Issue: Volume:11 Page:109841-109855
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Anand Pritam¹^ORCID,Jain Shantanu¹,Savaliya Harsh¹

Affiliation:

1. Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT), Gandhinagar, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10233867.pdf?arnumber=10233867

Reference39 articles.

1. Predictions of Missing Wave Data by Recurrent Neuronets

2. Large-margin Distribution Machine-based regression

3. Ocean wave height prediction using ensemble of Extreme Learning Machine

4. Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition

5. Empirical evaluation of gated recurrent neural networks on sequence modeling;chung;arXiv 1412 3555,2014

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP