| 基于LM-BP神经网络的Argo数据西北太平洋海水温度模型 |
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| 引用本文： | 韩震,赵宁.基于LM-BP神经网络的Argo数据西北太平洋海水温度模型[J].海洋环境科学,2012,31(4):555-560. |
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| 作者姓名： | 韩震 赵宁 |
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| 作者单位： | 1. 上海海洋大学海洋科学学院,上海201306;上海海洋大学大洋渔业资源可持续开发省部共建教育部重点实验室,上海201306
2. 上海海洋大学海洋科学学院,上海,201306 |
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| 基金项目： | 教育部科学技术研究重点项目，国家发改委高技术产业化示范工程项目 |
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| 摘 要： | 以2007年西北太平洋海域Argo海表面温度、经纬度、深度为输入参数,利用LM-BP神经网络,构建了西北太平洋海水温度模型。将均方根差以及Pearson相关性系数作为检验指标,利用2008年和2009年的Argo数据对模型进行了检验。检验结果为:2008年均方根误差为0.714 0℃,Pearson相关性系数为0.996 8;2009年均方根误差为0.761 5℃,Pearson相关性系数为0.9965。表明所建立的基于LM-BP神经网络的Argo数据西北太平洋海水温度模型是可行的。
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| 关 键 词： | 海水温度 西北太平洋 LM-BP神经网络 Argo数据 |
Seawater temperature model from Argo data by LM-BP neural network in Northwest Pacific Ocean |
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| HAN Zhen
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ZHAO Ning.Seawater temperature model from Argo data by LM-BP neural network in Northwest Pacific Ocean[J].Marine Environmental Science,2012,31(4):555-560. |
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| Authors: | HAN Zhen ZHAO Ning |
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| Institution: | 1(1.College of Marine Sciences,Shanghai Ocean University,Shanghai 201306,China;2.Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources,Ministry of Education,Shanghai Ocean University,Shanghai 201306,China) |
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| Abstract: | Using the LM-BP neural network and choosing the sea surface temperature,longitude,latitude and depth obtained from Argo data in 2007 as input parameters,the seawater temperature model of the Northwest Pacific Ocean was built.Using the root-mean-square error(RMSE) and the Pearson’s correlation coefficient(R) as test indices,the model was evaluated by the data in the period 2008 ~ 2009.The results were that the RMSE was 0.714 0 ℃ and R was 0.996 8 in 2008.The RMSE was 0.761 5 ℃ and R was 0.996 5 in 2009.It shown this seawater temperature model was. |
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| Keywords: | seawater temperature Northwest Pacific Ocean LM-BP neural network Argo data |
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