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

• Published in: EURASIP Journal on Wireless Communications and Networking
• Main Authors: Zhu, Linqian, Liu, Qi, Liu, Xiaodong, Zhang, Yonghong
• Other Authors: National College Students Innovation and Entrepreneurship Training Program, Jiangsu College Students Innovation and Entrepreneurship Training Program
• Format: Article in Journal/Newspaper
• Language: English
• Published: Springer Science and Business Media LLC 2021
• Subjects: Computer Networks and Communications; Computer Science Applications; Signal Processing; North Atlantic
• Online Access: http://dx.doi.org/10.1186/s13638-021-02044-9; https://link.springer.com/content/pdf/10.1186/s13638-021-02044-9.pdf; https://link.springer.com/article/10.1186/s13638-021-02044-9/fulltext.html

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.