Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems.
Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems.
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DOI：
10.1063/1.4916924
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发表时间：
2015-04
期刊：
影响因子：
2.9
通讯作者：
N. Marwan;J. Kurths
中科院分区：
文献类型：
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作者：
N. Marwan;J. Kurths
在这里，我们提出了两个有前途的技术应用的复杂网络的方法，连续的时空系统，在过去的十年中已经开发，并显示出巨大的潜力，未来的应用和发展的复杂系统分析。首先，我们讨论了时间序列从这样的系统到复杂网络的转换。自然的方法是计算递归矩阵，并解释相关复杂网络的邻接矩阵，称为递归网络。使用复杂网络的措施，如传递系数，我们证明，这种方法是非常有效的识别定性过渡的观测数据，例如，在分析古气候变迁时其次，我们展示了使用定向空间网络构建的时空测量系统，可以来自同步的极端事件在不同的空间区域发生。虽然有很多可能性，调查这样的空间网络，我们在这里提出的网络分歧的新措施，以及它如何可以用来开发一个极端降雨事件的预测方案。
We present here two promising techniques for the application of the complex network approach to continuous spatio-temporal systems that have been developed in the last decade and show large potential for future application and development of complex systems analysis. First, we discuss the transforming of a time series from such systems to a complex network. The natural approach is to calculate the recurrence matrix and interpret such as the adjacency matrix of an associated complex network, called recurrence network. Using complex network measures, such as transitivity coefficient, we demonstrate that this approach is very efficient for identifying qualitative transitions in observational data, e.g., when analyzing paleoclimate regime transitions. Second, we demonstrate the use of directed spatial networks constructed from spatio-temporal measurements of such systems that can be derived from the synchronized-in-time occurrence of extreme events in different spatial regions. Although there are many possibilities to investigate such spatial networks, we present here the new measure of network divergence and how it can be used to develop a prediction scheme of extreme rainfall events.