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High-resolution temperature and salinity model analysis using support vector regression

  • Yu Jiang
  • Tong Zhang
  • Yu Gou
  • Lili He
  • Hongtao Bai
  • Chengquan Hu
Original Research

Abstract

Temperature and salinity in marine data has been studied widely to enhance the marine environmental trend analysis. This work reports a new methodology for deriving high resolution monthly averages of temperature and salinity fields for the Berkeley Canyon based on the use of a support vector regression model. The data used in this paper is from WOA13, BOA Argo and ONC observations. The experimental results show that RBF Kernel function is satisfied with huge, complex marine data. The model is of great robust and adaptability. High resolution climatological means are critical for discerning oceanic features that are of great importance not just to climate systems but also to nutrient cycling and biological habitat. The method will be satisfied with other marine area.

Keywords

Temperature Salinity Support vector regression Underwater sensor network High resolution 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (51679105, 61672261, 51409117), Jilin Province Department of Education Thirteen Five science and technology research projects [2016] No. 432, [2017] No. JJKH20170804KJ.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yu Jiang
    • 1
    • 2
  • Tong Zhang
    • 1
    • 2
  • Yu Gou
    • 1
    • 2
  • Lili He
    • 1
    • 2
  • Hongtao Bai
    • 1
    • 2
  • Chengquan Hu
    • 1
    • 2
  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.Symbol Computation and Knowledge Engineer of Ministry of EducationJilin UniversityChangchunChina

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