| 对水文时间序列混沌特征参数估计问题的讨论 |
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| 引用本文： | 王文,许武成.对水文时间序列混沌特征参数估计问题的讨论[J].水科学进展,2005,16(4):609-616. |
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| 作者姓名： | 王文 许武成 |
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| 作者单位： | 1.河海大学水资源环境学院, 江苏, 南京, 210098; |
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| 摘 要： | 水文过程到底是不是低维混沌过程一直是个有争议的问题。相关文献在混沌特征参数估计中存在不少问题,包括:时延量估计的主观性很强,不同研究者的估计值差别很大;在关联维估计中,很多研究者有意无意地忽略了一个基本原则,即只有在关联维估计图上存在明确的标度区的情况下才能准确判断存在有限关联维;很多研究者在计算水文时间序列的关联维时仍采用原始的G-P计算公式,而没有采用Theiler提的修正公式,从而可能误将相点在时序上的相关性当做一种状态空间几何特征,造成关联维估计错误;国内相关研究中还普遍存在序列长度偏短的问题,对这些问题进行了讨论并给出了相应的结论。
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| 关 键 词： | 水文时间序列 混沌特性 关联维 嵌入维数 流量过程 |
| 文章编号： | 1001-6791(2005)04-0609-08 |
| 收稿时间： | 2004-03-18 |
| 修稿时间： | 2004年3月18日 |
Some issues on the characterization of chaotic properties of hydrologic time series |
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| WANG Wen,XU Wu-cheng.Some issues on the characterization of chaotic properties of hydrologic time series[J].Advances in Water Science,2005,16(4):609-616. |
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| Authors: | WANG Wen XU Wu-cheng |
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| Institution: | 1.Faculty of Water Resources and Environment, Hohai University, Nanjing 210098, China;2.Faculty of Land Resources, Xihua Normal University, Nanchong 637002, China |
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| Abstract: | Whether hydrologic processes are low-dimensional chaotic processes is still controversial. After analyzing the litera- ture about the research of chaos, we find that there are many problems. For instance, the estimation of the time delay is very subjective and application-dependent; in estimation of correlation dimension, many researchers ignore intentionally or careless- ly a principle, i.e,. there must be a clearly scaling region used for confirming the existence of finite correlation dimension; many researchers still use the original formula of G-P algorithm to calculate the correlation of hydrological time series instead of using the modified formula proposed by Theiler. Therefore, the temporally correlated points in the state space are probably mistaken for a spatial geometry in the state space, and the problem still commonly exits that the size of the studied series by researchers in China is too small. |
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| Keywords: | hydrologic time series chaotic property correlation dimension embedding dimension streamflow process |
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