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| 人工神经网络在南海近海面气温反演中的应用研究 | |
| 引用本文： | 吴新荣,韩桂军,张学峰,王喜冬.人工神经网络在南海近海面气温反演中的应用研究[J].热带海洋学报,2012(2):7-14. |
| 作者姓名： | 吴新荣 韩桂军 张学峰 王喜冬 |
| 作者单位： | 1. 国家海洋局海洋环境信息保障技术重点实验室,天津300171 2. 国家海洋信息中心,天津300171 3. 中国科学院南海海洋研究所,广东广州510301 4. 中国科学院研究生院,北京100039 |
| 基金项目： | 国家自然科学基金项目(41030854、40906015、40906016);国家科技支撑计划项目(2011BAC03B02-01-04) |
| 摘 要： | 基于人工神经网络方法,利用海面水温、海面风速以及海面气压反演南海近海面气温,采用的基础数据集是国际综合海洋-大气数据集(International Comprehensive Ocean-Atmosphere Data Set,2.4 Release,ICOADS2.4)1981—2008年的观测资料,其中1981—2000年的观测资料用来建立模型,2001—2008年的观测资料用来进行模型检验。采用的人工神经网络方法是引入动量因子并采用批处理梯度下降法的BP(Back propagation)算法。试验结果表明,基于人工神经网络建立的近海面气温反演方法明显优于多元线性回归方法,尤其是在春季和冬季,海面水温、海面风速以及海面气压与近海面气温之间存在较强的非线性关系,人工神经网络的优势更加明显。总体而言,人工神经网络在各月的反演效果较均衡,均方根误差介于1.5—1.8℃之间,平均绝对误差为1.1—1.3℃。 |
| 关 键 词： | 人工神经网络 BP算法 多元线性回归 |
Retrieving near-surface air temperature in the South China Sea using artificial neural network |
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| WU Xin-rong,HAN Gui-jun,ZHANG Xue-feng,WANG Xi-dong.Retrieving near-surface air temperature in the South China Sea using artificial neural network[J].Journal of Tropical Oceanography,2012(2):7-14. | |
| Authors: | WU Xin-rong HAN Gui-jun ZHANG Xue-feng WANG Xi-dong |
| Institution: | 1,2,3,4 1.Key Laboratory of Marine Environmental Information Technology,SOA,Tianjin 300171,China;2.National Marine Data and Information Service,Tianjin 300171,China;3.South China Sea Institute of Oceanology,Chinese Academy of Sciences,Guangzhou 510301,China;4.Graduate University of Chinese Academy of Sciences,Beijing 100039,China |
| Abstract: | Based on artificial neural network(ANN),the authors retrieved near-surface air temperature(AT) from sea surface temperature(SST),wind speed(WS) and sea level pressure(SLP) of the International Comprehensive Ocean-Atmosphere Dataset(ICOADS).Modeling sample spans from 1981 to 2000,while validating sample spans from 2001 to 2008.The adopted ANN introduces momentum factor to back propagation(BP) algorithm to escape from local extremes.In addition,batch processing gradient descent method was used to remove the effect of sequential training.Retrieving results in the South China Sea(SCS) demonstrates that ANN is better than multi-factor linear regression,especially for coastal areas during spring and winter,where strong non-linear relation exists between SST,WS,SLP and AT.In conclusion,ANN behaves similarly for each month,with root mean square error(RMSE) between 1.5℃ and 1.8℃ and mean absolute error(MAE) between 1.1℃ and 1.3℃. |
| Keywords: | Artificial neural network BP algorithm Multi-factor linear regression |
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