基于数据驱动模型的潮位和潮流预测方法研究

文章导航 >  北京理工大学学报自然版  > 2010  >  (7) : 864-868.

李明昌, 梁书秀, 孙昭晨, 张光玉. 基于数据驱动模型的潮位和潮流预测方法研究[J]. 北京理工大学学报自然版, 2010, (7): 864-868.

引用本文: 李明昌, 梁书秀, 孙昭晨, 张光玉. 基于数据驱动模型的潮位和潮流预测方法研究[J]. 北京理工大学学报自然版, 2010, (7): 864-868.

LI Ming-chang, LIANG Shu-xiu, SUN Zhao-chen, ZHANG Guang-yu. Tidal Level and Current Prediction on the Basis of Data-Driven Model[J]. Transactions of Beijing institute of Technology, 2010, (7): 864-868.

Citation: LI Ming-chang, LIANG Shu-xiu, SUN Zhao-chen, ZHANG Guang-yu. Tidal Level and Current Prediction on the Basis of Data-Driven Model[J]. Transactions of Beijing institute of Technology, 2010, (7): 864-868.

基于数据驱动模型的潮位和潮流预测方法研究

1.

交通部天津水运工程科学研究院 水路交通环境保护技术实验室, 天津 300456;大连理工大学 海岸和近海工程国家重点实验室, 辽宁,大连 116024

2.

大连理工大学 海岸和近海工程国家重点实验室, 辽宁,大连 116024

3.

交通部天津水运工程科学研究院 水路交通环境保护技术实验室, 天津 300456

基金项目: 中央级公益性科研院所基本科研业务费专项基金资助项目(TKS090204,TKS100217)

计量 文章访问数:  01645 HTML全文浏览量:  024 PDF下载量:  0128 出版历程 收稿日期:  2009-11-12

Tidal Level and Current Prediction on the Basis of Data-Driven Model

1.

Laboratory of Environmental Protection in Water Transport Engineering, Tianjin Research Institute of Water Transport Engineering, Tianjin 300456, China;State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, Liao

2.

State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China

3.

Laboratory of Environmental Protection in Water Transport Engineering, Tianjin Research Institute of Water Transport Engineering, Tianjin 300456, China

摘要

摘要: 为解决工程海域潮位、潮流资料不足给海洋工程设计和数学模型建立带来的不确定性,根据单测站潮位潮流的自相关性、对应测站潮位或潮流以及潮位与潮流之间的互相关性,建立基于数据驱动模型人工神经网络的单测站潮位、潮流(流速、流向)预测模型;多测站潮位、潮流对应预测模型;潮位与潮流对应预测模型. 以复杂海况下的实测潮位、潮流资料进行模型验证,重现了潮位、潮流自身及相互之间的非线性映射关系. 模型预测结果与现场实测数据的比较及其误差分析表明,该模型具有结构简单、精度高的优点,适用于解决工程实际问题.

Abstract: The insufficiency of tidal level and current data near the ocean engineering waters may bring uncertainty for ocean engineering design and numerical model construction. To solve this problem, some of necessary prediction models are developed, including one site tidal level or current (current velocity and current direction) prediction model, multi-site tidal level or current prediction model, and tidal level-current prediction model. The construction of those models is based on the back propagation (BP) artificial neural network (ANN) and the properties of self-correlation of single site and cross-correlation among multi-sites or between tidal level and current. Field data under complex geography and hydrodynamic condition are used to validate the performance of the presented data-driven models. It is indicated that the nonlinear mapping relation between tidal level and tidal current can be reproduced by those models. The comparison between the numerical results and field-data verifies that the developed models have the advantages of simple structure and good precision.

HTML全文

参考文献(11)

[1] Solomatine D P. Data-driven modelling: paradigm, methods, experiences //Proc 5th International Conference on Hydroinformatics. Cardiff, UK: , 2002:757-763. [2] Deo M C,Naidu S C. Real time wave forecasting using neural networks[J]. Ocean Engineering, 1999,26(3):191-203. [3] Lee T L, Tsai C P, Jeng D S, et al. Neural network for the prediction and supplement of tidal record in Taichung Harbor, Taiwan[J]. Advances in Engineering Software, 2002,33(6):329-338. [4] Huang W R, Catherine M, Nicholas K, et al.Development of a regional neural network for coastal water level predictions[J]. Ocean Engineering, 2003,30(17):2275-2295. [5] 胡继洋,李启华,王宇浩.基于神经网络的潮汐预报方法初探[J].海洋测绘,2005,25(6):48-50. Hu Jiyang, Li Qihua, Wang Yuhao. Research on tide prediction method based on neural network[J]. Hydrographic Surveying and Charting, 2005,25(6):48-50. (in Chinese) [6] 李明昌,梁书秀,孙昭晨.人工神经网络在潮汐预测中应用研究[J].大连理工大学学报,2007,47(1):101-105. Li Mingchang, Liang Shuxiu, Sun Zhaochen. Application of artificial neural networks to tide forecasting[J]. Journal of Dalian University of Technology, 2007,47(1):101-105. (in Chinese) [7] 阎平凡,张长水.人工神经网络与模拟进化计算[M].北京:清华大学出版社,2005. Yan Pingfan, Zhang Changshui. Artificial neural networks and evolutionary computing[M]. Beijing: Tsinghua University Press, 2005. (in Chinese) [8] Lee T L, Jeng D S. Application of artificial neural networks in tide forecasting[J]. Ocean Engineering, 2002,29(9):1003-1022. [9] 徐汉兴.潮汐计算[M].北京:人民交通出版社,1998. Xu Hanxing. Tide computation[M]. Beijing: China Communications Press, 1998. (in Chinese) [10] Center for Operational Oceanographic Products and Services, National Oceanic and Atmospheric Administration. Tides and currents data . . http://co-ops.nos.noaa.gov. [11] 董胜,孔令双.海洋工程环境概论[M].青岛:中国海洋大学出版社,2005:110-159. Dong Sheng, Kong Lingshuang. Conspectus on ocean engineering environment[M]. Qingdao: China Ocean University Press, 2005:110-159. (in Chinese)

施引文献

资源附件(0)

计量 文章访问数:  HTML全文浏览量:  PDF下载量:  出版历程 收稿日期:  2009-11-12

网址：基于数据驱动模型的潮位和潮流预测方法研究 https://mxgxt.com/news/view/2067046

相关内容

大数据时代的时尚潮流预测
明星IP驱动下的中国潮流文化品牌建设研究
基于机器学习的离婚预测模型及应用研究
基于数据分析的英超球员转会动态追踪与预测系统开发研究
揭秘人群方舟算法：如何精准预测潮流趋势？
基于社交网络数据的电影票房预测研究
温度/化学浓度驱动流体流动的数值模拟研究
基于BG/NBD模型的用户生命周期价值预测与实证研究
基于Galaxy平台及miRspring的测序数据分析技术研究
基于复杂网络和可视图结构的股指极端值预测方法研究

随便看看