RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction

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 var...

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Bibliographic Details
Published in:EURASIP journal on wireless communications and networking Vol. 2021; no. 1; pp. 1 - 18
Main Authors: Zhu, Linqian, Liu, Qi, Liu, Xiaodong, Zhang, Yonghong
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 26.08.2021
Springer Nature B.V
SpringerOpen
Subjects:
Accuracy
Adaptability
Algorithms
Atmospheric reflection
Communications Engineering
Emission analysis
Engineering
Environmental protection
Global warming
Greenhouse gases
Grey model
Industrial development
Information Systems Applications (incl.Internet)
Long-term prediction
Mathematical analysis
Methods
Model accuracy
Multi-modal Sensor Data Fusion in Internet of Things
Natural resources
Networks
Neural networks
Precipitation
Radiation
Regional SST
Sea surface temperature
Seawater
Signal,Image and Speech Processing
Simulation
Temperature variation
Trends
Wireless networks
Regional SST
Atmospheric reflection
Grey model
Long-term prediction
Temperature variation
ISSN:1687-1499, 1687-1472, 1687-1499
Online Access:Get full text
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Summary: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 ∘ C by 2070. This model is easy to calculate, with advantages of the high accuracy and good robustness.
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ISSN:1687-1499
1687-1472
1687-1499
DOI:10.1186/s13638-021-02044-9