A simulation model of seawater vertival temperature by using back-propagation neural network

• Autorzy: Zhao N. , Han Z.
• Języki publikacji: EN

Abstrakty

EN

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%

• Wydawca: -
• Czasopismo: Polish Maritime Research
• Rocznik: 2015
• Tom: 22
• Numer: Special Issue S1
• Opis fizyczny: p.82-88,fig.,ref.

Twórcy

autor

Zhao N.

• College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
• Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga 8168580, Japan

autor

Han Z.

• 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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Identyfikatory

DOI

10.1515/pomr-2015-0037