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应用神经网络技术预报海表温度变化异常
  • 期刊名称：中国科学院上海天文台年刊
  • 时间：0
  • 页码：11-20
  • 语言：中文
  • 分类：P731.31[天文地球—海洋科学]
  • 作者机构：[1]中国科学院上海天文台 中南大学测绘与国土信息工程系
  • 相关基金：国家自然科学基金(No.10673025,No.10633030,No.10878026)项目资助课题
  • 相关项目：地表流体对地球自转变化的激发
作者： 廖德春，王琪洁，周永宏， 廖新浩|
关键词： ENSO, SSTA, 神经网络, 预报精度, ENSO — SSTA — neural networks — prediction accuracy
中文摘要：

介绍了神经网络技术在时间序列模拟和预报中的应用要点;应用神经网络技术对Nino3.4海区海表温度变化异常(SSTA)进行了实验预报,每次给出1～12个月预报值;将预报值与SSTA实测值以及其它模型的预报值进行了比较。结果表明,在1990年1月～2007年10月期间,SSTA预报值与实测值的均方误差为±0.76℃。将2004年10月～2007年10月期间该方法的预报结果与美国气候预测中心(CPC)发布的各种模型的预报结果(只给出第2～10月预报值)进行比较,结果表明这一期间该方法预报值相对实测值的均方误差为±0.86℃,精度略好于另外一个神经网络模型的预报精度(±0.98℃)。

英文摘要：

Applications of the neural networks in time series forecasting are introduced in this paper. Based on the neural network models,predictions of the sea surface temperature anomaly (SSTA) in Nino3.4 region of tropical Pacific are made in this study. SSTA values for future 12 months are predicted for every month. The root mean square error (RMSE) between our SSTA predictions and observations is about ±0.76℃ for Jan. 1990～Oct. 2007. Our RMSE result (±0.86℃) is slightly small in comparison with ±0.98℃ which is o...

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快速旋转行星流体动力学
期刊论文 52 会议论文 1 著作 1
地球自转变化的ANN预报
期刊论文 20 会议论文 5 获奖 2
地表流体对地球自转变化的激发
期刊论文 18 会议论文 6
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