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%.
A Simulation Model of Seawater Vertical Temperature by Using Back-Propagation Neural Network
2015
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
A Simulation Model of Seawater Vertical Temperature by Using Back-Propagation Neural Network
Online Contents | 2015
|Simulation of Driver Behavior with Agent-Based Back-Propagation Neural Network
Online Contents | 2011
|Simulation of Driver Behavior with Agent-Based Back-Propagation Neural Network
Transportation Research Record | 2011
|Analog Delta-Back-Propagation Neural-Network Circuitry
NTRS | 1990
|Fault Diagnosis of Rotating Machinery Using Back Propagation Neural Network
British Library Online Contents | 1997
|