基于多元时间序列的河流混沌特性研究

发布时间：2018-07-02 15:00

本文选题：河流 + 宽深比　；参考：《泥沙研究》2017年03期

【摘要】：以混沌理论为基础,提出了河流混沌特性分析方法。选择对河流演变有重要影响的宽深比时间序列和水沙时间序列,首先对这些时间序列进行相空间重构,计算不同河型的宽深比、径流量和含沙量时间序列的饱和关联维数和最大Lyapunov指数,然后通过求这些时间序列的饱和关联维数的加权平均值和最大Lyapunov指数的加权平均值,得出不同河型的混沌特性。以黄河下游的6个河段3种河型为例,对宽深比、径流量和含沙量时间序列,分别进行混沌特性分析。研究结果表明,河流演变具有明显的混沌特性,但不同河型表现出的混沌特性不同,游荡河型混沌特性较强,弯曲河型混沌特性较弱。通过对河流混沌特性分析,有助于加深对河流演变预测的进一步认识。根据混沌理论,混沌系统短期行为可以预测,而长期不能预测。所以,河流演变预测是短期可行,长期很难预测、甚至是不可预测的。
[Abstract]:Based on the chaos theory, a method for analyzing the chaotic characteristics of rivers is proposed. The time series of width to depth ratio and the time series of water and sediment which have important influence on river evolution are selected. Firstly, the phase space reconstruction of these time series is carried out to calculate the ratio of width to depth of different river types. The saturation correlation dimension and the maximum Lyapunov exponent of the time series of runoff and sediment content are obtained, and the chaotic characteristics of different river types are obtained by calculating the weighted average of the saturated correlation dimension and the weighted average of the maximum Lyapunov exponent of these time series. Taking three types of rivers in 6 reaches of the lower reaches of the Yellow River as examples, the chaotic characteristics of time series of width to depth ratio, runoff and sediment content are analyzed respectively. The results show that the evolution of rivers has obvious chaotic characteristics, but the chaotic characteristics of different river types are different, the wandering river type chaotic characteristics are stronger, and the curved river type chaotic characteristics are weak. By analyzing the chaotic characteristics of rivers, it is helpful to deepen the understanding of river evolution prediction. According to chaos theory, the short-term behavior of chaotic system can be predicted, but the long-term can not be predicted. Therefore, river evolution prediction is feasible in the short-term, difficult to predict in the long-term, even unpredictable.
【作者单位】：天津大学水利工程仿真与安全国家重点实验室;大禹节水(天津)有限公司;
【基金】：国家自然科学基金创新研究群体科学基金资助项目(51321065);国家自然科学基金项目(50679053)
【分类号】：TV147

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