A SIMULATION MODEL OF SEAWATER VERTICAL TEMPERATURE BY USING BACK-PROPAGATION NEURAL NETWORK

被引：6

作者：

Zhao, Ning ^{[1
,2
]}

Han, Zhen ^{[1
,3
]}

机构：

[1] Shanghai Ocean Univ, Coll Marine Sci, Shanghai 201306, Peoples R China

[2] Kyushu Univ, Interdisciplinary Grad Sch Engn Sci, Kasuga, Fukuoka 8168580, Japan

[3] Shanghai Ocean Univ, Collaborat Innovat Ctr Distant Water Fisheries, Shanghai 201306, Peoples R China

来源：

POLISH MARITIME RESEARCH | 2015年 / 22卷

关键词：

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

D O I：

10.1515/pomr-2015-0037

中图分类号：

U6 [水路运输]; P75 [海洋工程];

学科分类号：

0814 ; 081505 ; 0824 ; 082401 ;

摘要：

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 degrees 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 degrees C. When counting with errors lower than +/- 1 degrees C, the lowest percentage was 83%.

引用

页码：82 / 88

页数：7

共 14 条

[1]

AVISO, 2012, CLSDOSNT06034 AVISO, P39

[2] Linear and non-linear T-S models for the eastern North Atlantic from Argo data: Role of surface salinity observations [J].