RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction
Abstract For the purpose of exploring the long-term variation of regional sea surface temperature (SST), this paper studies the historical SST in regional sea areas and the emission pattern of greenhouse gases, proposing a Grey model of regional SST atmospheric reflection which can be used to predic...
Published in: | EURASIP Journal on Wireless Communications and Networking |
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ftdoajarticles:oai:doaj.org/article:8c45638a24a54114af7648bb2617f296 2023-05-15T17:33:26+02:00 RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction Linqian Zhu Qi Liu Xiaodong Liu Yonghong Zhang 2021-08-01T00:00:00Z https://doi.org/10.1186/s13638-021-02044-9 https://doaj.org/article/8c45638a24a54114af7648bb2617f296 EN eng SpringerOpen https://doi.org/10.1186/s13638-021-02044-9 https://doaj.org/toc/1687-1499 doi:10.1186/s13638-021-02044-9 1687-1499 https://doaj.org/article/8c45638a24a54114af7648bb2617f296 EURASIP Journal on Wireless Communications and Networking, Vol 2021, Iss 1, Pp 1-18 (2021) Long-term prediction Regional SST Temperature variation Grey model Atmospheric reflection Telecommunication TK5101-6720 Electronics TK7800-8360 article 2021 ftdoajarticles https://doi.org/10.1186/s13638-021-02044-9 2022-12-31T07:39:25Z Abstract For the purpose of exploring the long-term variation of regional sea surface temperature (SST), this paper studies the historical SST in regional sea areas and the emission pattern of greenhouse gases, proposing a Grey model of regional SST atmospheric reflection which can be used to predict SST variation in a long time span. By studying the Grey systematic relationship between historical SST data, the model obtains the development law of temperature variation, and further introduces different greenhouse gas emission scenarios in the future as the indexes coefficient to determine the corresponding changing results of seawater temperature in the next 50 years. Taking the North Atlantic Ocean as an example, the cosine similarity test method is used to verify the model proposed in this paper. The accuracy of the model is as high as 0.99984. The model predicts that the regional SST could reach a maximum of $$15.3\,^{\circ }{\mathrm {C}}$$ 15.3 ∘ C by 2070. This model is easy to calculate, with advantages of the high accuracy and good robustness. Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles EURASIP Journal on Wireless Communications and Networking 2021 1 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Long-term prediction Regional SST Temperature variation Grey model Atmospheric reflection Telecommunication TK5101-6720 Electronics TK7800-8360 |
spellingShingle |
Long-term prediction Regional SST Temperature variation Grey model Atmospheric reflection Telecommunication TK5101-6720 Electronics TK7800-8360 Linqian Zhu Qi Liu Xiaodong Liu Yonghong Zhang RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction |
topic_facet |
Long-term prediction Regional SST Temperature variation Grey model Atmospheric reflection Telecommunication TK5101-6720 Electronics TK7800-8360 |
description |
Abstract For the purpose of exploring the long-term variation of regional sea surface temperature (SST), this paper studies the historical SST in regional sea areas and the emission pattern of greenhouse gases, proposing a Grey model of regional SST atmospheric reflection which can be used to predict SST variation in a long time span. By studying the Grey systematic relationship between historical SST data, the model obtains the development law of temperature variation, and further introduces different greenhouse gas emission scenarios in the future as the indexes coefficient to determine the corresponding changing results of seawater temperature in the next 50 years. Taking the North Atlantic Ocean as an example, the cosine similarity test method is used to verify the model proposed in this paper. The accuracy of the model is as high as 0.99984. The model predicts that the regional SST could reach a maximum of $$15.3\,^{\circ }{\mathrm {C}}$$ 15.3 ∘ C by 2070. This model is easy to calculate, with advantages of the high accuracy and good robustness. |
format |
Article in Journal/Newspaper |
author |
Linqian Zhu Qi Liu Xiaodong Liu Yonghong Zhang |
author_facet |
Linqian Zhu Qi Liu Xiaodong Liu Yonghong Zhang |
author_sort |
Linqian Zhu |
title |
RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction |
title_short |
RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction |
title_full |
RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction |
title_fullStr |
RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction |
title_full_unstemmed |
RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction |
title_sort |
rsst-argm: a data-driven approach to long-term sea surface temperature prediction |
publisher |
SpringerOpen |
publishDate |
2021 |
url |
https://doi.org/10.1186/s13638-021-02044-9 https://doaj.org/article/8c45638a24a54114af7648bb2617f296 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
EURASIP Journal on Wireless Communications and Networking, Vol 2021, Iss 1, Pp 1-18 (2021) |
op_relation |
https://doi.org/10.1186/s13638-021-02044-9 https://doaj.org/toc/1687-1499 doi:10.1186/s13638-021-02044-9 1687-1499 https://doaj.org/article/8c45638a24a54114af7648bb2617f296 |
op_doi |
https://doi.org/10.1186/s13638-021-02044-9 |
container_title |
EURASIP Journal on Wireless Communications and Networking |
container_volume |
2021 |
container_issue |
1 |
_version_ |
1766131942094274560 |