基于时间卷积网络和LSTM-GRU网络的海温预测

Yu Jiang, Minghao Zhao, Wanting Zhao, Hongde Qin, Hong Qi, Kai Wang, Chong Wang
{"title":"基于时间卷积网络和LSTM-GRU网络的海温预测","authors":"Yu Jiang, Minghao Zhao, Wanting Zhao, Hongde Qin, Hong Qi, Kai Wang, Chong Wang","doi":"10.20517/ces.2021.03","DOIUrl":null,"url":null,"abstract":"Prediction of temperature Abstract The ocean is a complex system. Ocean temperature is an important physical property of seawater, so studying its variation is of great significance. Two kinds of network structures for predicting thermocline time series data are proposed in this paper. One is the LSTM-GRU hybrid neural network model, and the other is the temporal convolutional network (TCN) model. The two networks have obvious advantages over other models in accuracy, stability, and adaptability. Compared with the traditional auto-regressive integrate moving average model, the proposed method considers the influence of temperature history, salinity, depth, and other information. The experimental results show that TCN performs better in prediction accuracy, while LSTM-GRU can better predict abnormal data and has higher robustness.","PeriodicalId":72652,"journal":{"name":"Complex engineering systems (Alhambra, Calif.)","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Prediction of sea temperature using temporal convolutional network and LSTM-GRU network\",\"authors\":\"Yu Jiang, Minghao Zhao, Wanting Zhao, Hongde Qin, Hong Qi, Kai Wang, Chong Wang\",\"doi\":\"10.20517/ces.2021.03\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Prediction of temperature Abstract The ocean is a complex system. Ocean temperature is an important physical property of seawater, so studying its variation is of great significance. Two kinds of network structures for predicting thermocline time series data are proposed in this paper. One is the LSTM-GRU hybrid neural network model, and the other is the temporal convolutional network (TCN) model. The two networks have obvious advantages over other models in accuracy, stability, and adaptability. Compared with the traditional auto-regressive integrate moving average model, the proposed method considers the influence of temperature history, salinity, depth, and other information. The experimental results show that TCN performs better in prediction accuracy, while LSTM-GRU can better predict abnormal data and has higher robustness.\",\"PeriodicalId\":72652,\"journal\":{\"name\":\"Complex engineering systems (Alhambra, Calif.)\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Complex engineering systems (Alhambra, Calif.)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.20517/ces.2021.03\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Complex engineering systems (Alhambra, Calif.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20517/ces.2021.03","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

海洋是一个复杂的系统。海洋温度是海水的重要物理性质,研究其变化具有重要意义。本文提出了两种预测温跃层时间序列数据的网络结构。一种是LSTM-GRU混合神经网络模型,另一种是时间卷积网络(TCN)模型。与其他模型相比,这两种网络在精度、稳定性和适应性方面具有明显的优势。与传统的自回归积分移动平均模型相比,该方法考虑了温度历史、盐度、深度等信息的影响。实验结果表明,TCN具有更好的预测精度,而LSTM-GRU能够更好地预测异常数据,具有更高的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of sea temperature using temporal convolutional network and LSTM-GRU network
Prediction of temperature Abstract The ocean is a complex system. Ocean temperature is an important physical property of seawater, so studying its variation is of great significance. Two kinds of network structures for predicting thermocline time series data are proposed in this paper. One is the LSTM-GRU hybrid neural network model, and the other is the temporal convolutional network (TCN) model. The two networks have obvious advantages over other models in accuracy, stability, and adaptability. Compared with the traditional auto-regressive integrate moving average model, the proposed method considers the influence of temperature history, salinity, depth, and other information. The experimental results show that TCN performs better in prediction accuracy, while LSTM-GRU can better predict abnormal data and has higher robustness.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
1.70
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信