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学科主题: Computer Science, Interdisciplinary Applications; Engineering, Civil; Environmental Sciences; Water Resources
题名: Incorporation of artificial neural networks and data assimilation techniques into a third-generation wind-wave model for wave forecasting
作者: Zhang, ZX ; Li, CW ; Qi, YQ ; Li, YS
通讯作者: cecwli@polyu.edu.hk
关键词: artificial neural networks ; data assimilation ; wave forecast ; wind-wave model
刊名: JOURNAL OF HYDROINFORMATICS
发表日期: 2006
卷: 8, 期:1, 页:65-76
收录类别: sci
部门归属: Hong Kong Polytech Univ, Dept Civil & Struct Engn, Hong Kong, Hong Kong, Peoples R China; Chinese Acad Sci, S China Sea Inst Oceanol, Guangzhou, Peoples R China; Hong Kong Polytech Univ, Dept Civil & Struct Engn, Hong Kong, Hong Kong, Peoples R China
项目归属: LED
摘要: Although the third-generation formulation of the ocean wave model describes the wave generation, dissipation and nonlinear interaction processes explicitly, many empirical parameters exist in the model which have to be determined experimentally. With the advance in oceanographic remote-sensing techniques, information on oceanic parameters including significant wave height (SWH) can be obtained daily by satellite altimeters The assimilation of these data into the wave model provides a way of improving the hindcasting results. However, for wave forecasting, no altimeter data exist during the forecasting period by definition To improve the forecasting accuracy of the wave model, Artificial Neural Networks (ANN) are introduced to mimic the errors introduced by the wave model This is achieved by training the ANN using the wave model output as input, and the results after data assimilation as the targeted output. The trained ANN is then used as a post-processor of the output from the wave model. The proposed method has been applied in wave simulation in the northwestern Pacific Ocean. The statistical interpolation method is used to assimilate the altimeter data into the wave model output and a back-propagation ANN is used to mimic the relation between the wave model outputs with or without data assimilation. The results show that an apparent improvement in the accuracy of forecasting can be obtained.
原文出处: 查看原文
WOS记录号: WOS:000235110800006
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内容类型: 期刊论文
URI标识: http://ir.scsio.ac.cn/handle/344004/5819
Appears in Collections:热带海洋环境动力实验室(LTO)_期刊论文

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Recommended Citation:
Zhang, ZX; Li, CW; Qi, YQ; Li, YS.Incorporation of artificial neural networks and data assimilation techniques into a third-generation wind-wave model for wave forecasting,JOURNAL OF HYDROINFORMATICS,2006,8(1):65-76
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