水资源与水工程学报

文章摘要

LI Mingchang,SI Qi,LIANG Shuxiu,SUN Zhaochen,YOU Xueyi.海域潮流场数值模型开边界条件补遗预测的应用数据驱动模型Journal of Water Resources and Water Engineering[J].,2011,22(1):

海域潮流场数值模型开边界条件补遗预测的应用数据驱动模型

Applied data-driven model for current field calibration under the condintion of open boundary

DOI：

中文关键词: 数据驱动模型人工神经网络潮流场模型反演开边界条件

英文关键词: data-driven model ANN POM model inversion open boundary condition

基金项目:中央级公益性科研院所基本科研业务费专项资金项目(TKS090204TKS100217)

Author Name

Affiliation

LI Mingchang,SI Qi,LIANG Shuxiu,SUN Zhaochen,YOU Xueyi

LI Mingchang1,2,SI Qi2,LIANG Shuxiu3,SUN Zhaochen3,YOU Xueyi1(1.School of Environmental Science and Engineering,Tianjin University,Tianjin 300072,China,2.Laboratory of EnvironmentalProtection in Water Transport Engineering,Tianjin Research Institute of Water Transport Engineering,Tianjin 300456,3.State Key Laboratory of Coastal and Offshore Engineering,Dalian University of Technology,Dalian 116024,China)

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中文摘要:

利用POM(Princeton Ocean Model)等数值模型计算海域潮流场需要开边界条件来驱动模型,开边界条件的确定是主要难点之一。本文建立基于人工神经网络的数据驱动模型为海域潮流场数值模型验证提供开边界条件。通过实际海域计算验证模型,取得了良好的效果。

英文摘要:

Open boundary condition is the key point for simulation of current field and is difficult to decide.This paper presented a data-driven model implemented by artificial neural network(ANN) to provide the open boundary condition for numerical model such as POM.The calibration results DDM work well.

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