面向海洋数据的复杂网络建模及可视化分析

doi:10.16182/j.issn1004731x.joss.201807002

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (7): 2445-2452.doi: 10.16182/j.issn1004731x.joss.201807002

面向海洋数据的复杂网络建模及可视化分析

孙鑫, 李振华, 董军宇, 罗新艳, 杨玉婷

中国海洋大学计算机科学与技术系,山东青岛 266100

收稿日期:2017-08-14 出版日期:2018-07-10 发布日期:2019-01-08
作者简介:孙鑫(1984-), 男, 山东淄博, 博士后, 副教授,研究方向为复杂网络和机器学习;李振华(1991-),男,山东菏泽,硕士生,研究方向为复杂网络。
基金资助:
国家自然科学基金(41741007,41576011),山东省重点研发计划(GG201703140154)

Complex Network Modeling and Visualization Analysis for Ocean Observation Data

Sun Xin, Li Zhenhua, Dong Junyu, LuoXinyan, Yang Yuting

Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China

Received:2017-08-14 Online:2018-07-10 Published:2019-01-08

摘要/Abstract

摘要： 海洋数据分析是海洋科学研究的重要基础之一,基于复杂网络理论研究海洋表面温度有助于从新的视角探究海洋的动态变化。将全球海洋进行固定尺度网格划分,均值化每个网格内的海表面温度,利用互信息和皮尔逊相关系数度量不同海域间海温时序的相似性,构建了全球海洋气候的非线性和线性复杂网络模型,仿真对比了两者的拓扑性质。利用度分布、聚类系数和介数等统计特征可视化海洋不同海域的能量传递等现象,分析和探索了海洋气候的系统稳定性和季节性差异等。

关键词: 复杂网络, 可视化, 时间序列, 互信息, 皮尔逊相关系数, 海表温度, 拓扑仿真

Abstract: Ocean data analysis is one of the important foundations in marine science research. Analysis on the sea surface temperature based on complex network theory helps explore the marine dynamics in a new perspective. The ocean is divided into grids, and the annual average of the sea surface temperature is calculated to reflect the properties of the corresponding grid area. The mutual information and the Pearson correlation coefficient are used to measure the similarity between different areas. The nonlinear and linear complex network models which reflect the station of the global marine climate can be built. Finally some popular measures including degree distribution, clustering coefficient and betweenness are introduced to discover the ocean phenomena, such as energy transfer of ocean, and the system robust and seasonal variation of the ocean dynamics are analyzed.

Key words: complex networks, visualization, time series, mutual information, Pearson correlation coefficient, sea surface temperature, topological simulation

中图分类号:

TP391

孙鑫, 李振华, 董军宇, 罗新艳, 杨玉婷. 面向海洋数据的复杂网络建模及可视化分析[J]. 系统仿真学报, 2018, 30(7): 2445-2452.

Sun Xin, Li Zhenhua, Dong Junyu, LuoXinyan, Yang Yuting. Complex Network Modeling and Visualization Analysis for Ocean Observation Data[J]. Journal of System Simulation, 2018, 30(7): 2445-2452.

参考文献

[1] Lorenz EN.REFLECTIONS ON THE CONCEPTION, BIRTH, AND CHILDHOOD OF NUMERICAL WEATHER PREDICTION[J]. Annual Review of Earth & Planetary Sciences(S0084-6597), 2006, 34(1): 37-45.
[2] 张春玲, 李宏, 许建平, 等. 海洋温盐度资料多变量同化研究进展[J]. 海洋预报, 2013, 30(1): 86-92.
Chunling Zhang, Hong Li, Jianping Xu, et al.Research Progress on Multivariate Assimilation of Ocean Temperature and Salinity Data[J]. Ocean forecast, 2013, 30(1): 86-92.
[3] Owens D, Best M, Guillemot E, et al.Ocean observatories and social computing: Potential and progress[C]. USA: IEEE(S0197-7385), 2010: 1-9.
[4] Tominski C, Donges J F, Nocke T.Information Visualization in Climate Research[C]. USA: IEEE (S1550-6037), 2011: 298-305.
[5] Donges J F, Petrova I, Loew A, et al.How complex climate networks complement eigen techniques for the statistical analysis of climatological data[J]. Climate Dynamics(S0930-7575), 2015, 45(9/10): 2407-2424.
[6] Steinhaeuser K, Chawla N V, Ganguly A R.An exploration of climate data using complex networks[J]. Acm Sigkdd Explorations Newsletter (S1931-0145), 2010, 12(1): 25-32.
[7] Donges J F, Zou Y, Marwan N, et al.The backbone of the climate network[J]. EPL (S0295-5075), 2009, 87: 48007.
[8] Boers N, Bookhagen B, Marwan N, et al.Complex networks identify spatial patterns of extreme rainfall events of the South American Monsoon System[J]. Geophysical Research Letters (S0094-8276), 2013, 40(16): 4386-4392.
[9] Ludescher J, Gozolchiani A, Bogachev M I, et al.Very early warning of next El Nino[J]. Proceedings of the National Academy of Sciences (S0027-8424), 2014, 111(6): 2064-2066.
[10] Boers N, Bookhagen B, Barbosa H M J, et al. Prediction of extreme floods in the eastern Central Andes based on a complex networks approach[J]. Nature Communications (S2041-1723), 2014, 5: 5199.
[11] Feng Q Y, Dijkstra H A.Climate network stability measures of El Niño variability[J]. Chaos(S1054-1500), 2017, 27(3): 035801.
[12] Wiedermann M, Radebach A, Donges J F, et al.A climate network-based index to discriminate different types of El Niño and La Niña[J]. Geophysical Research Letters (S0094-8276), 2016, 43(13): 7176-7185.
[13] Tsonis A A, Swanson K L, Wang G.On the Role of Atmospheric Teleconnections in Climate[J]. Journal of Climate (S0894-8755), 2008, 21(12): 2990-3001.
[14] Tsonis A A, Roebber P J.The architecture of the climate network[J]. Physica A Statistical Mechanics & Its Applications (S0378-4371), 2004, 333(4): 497-504.
[15] Tsonis A A, Swanson K L.Topology and predictability of El Niño and La Niña networks[J]. Physical Review Letters(S0031-9007), 2008, 100(22): 228502.
[16] Deza J I, Ihshaish H.The Construction of Complex Networks from Linear and Nonlinear Measures-Climate Networks[J]. Procedia Computer Science (S1877-0509), 2015, 51: 404-412.
[17] Steinhaeuser K, Chawla N V, Ganguly A R.Complex Networks In Climate Science: Progress, Opportunities And Challenges[C]. USA:Conference on Intelligent Data Understanding, 2010: 16-26.
[18] Hlinka J, Hartman D, Jajcay N, et al.Small-world bias of correlation networks: From brain to climate[J]. Chaos (S1054-1500), 2017, 27(3): 035812.
[19] Donges J F, Zou Y, Marwan N, et al.Complex networks in climate dynamics[J]. The European Physical Journal Special Topics (S1951-6355), 2009, 174(1): 157-179.

面向海洋数据的复杂网络建模及可视化分析

Complex Network Modeling and Visualization Analysis for Ocean Observation Data

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