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Polish Maritime Research

The Journal of Gdansk University of Technology

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IMPACT FACTOR 2016: 0.776

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Volume 22, Issue s1 (Sep 2015)


A Simulation Model of Seawater Vertical Temperature by Using Back-Propagation Neural Network

M. S. Ning Zhao
  • College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
  • Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga 8168580, Japan
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/ Ph. D Zhen Han
  • College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
  • Collaborative Innovation Center for Distant-water Fisheries, Shanghai Ocean University, Shanghai 201306, China
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Published Online: 2015-10-15 | DOI: https://doi.org/10.1515/pomr-2015-0037


This study proposed a neural-network-based model to estimate the ocean vertical water temperature from the surface temperature in the northwest Pacific Ocean. The performance of the model and the sources of errors were assessed using the Gridded Argo dataset including 576 stations with 26 vertical levels from surface (0 m)–2,000 m over the period of 2007–2009. The parameter selection, model building, stability of the neural network were also investigated. According to the results, the averaged root mean square error (RMSE) of estimated temperature was 0.7378 °C and the correlation coefficient R was 0.9967. More than 67% of the estimates from the four selected months (January, April, July and October) lay within ± 0.5 °C. When counting with errors lower than ± 1°C, the lowest percentage was 83%.

Keywords: neural network; Agro data; vertical structure; surface temperature


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About the article

Published Online: 2015-10-15

Published in Print: 2015-09-01

Citation Information: Polish Maritime Research, ISSN (Online) 2083-7429, DOI: https://doi.org/10.1515/pomr-2015-0037.

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© 2015. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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