首页> 外文期刊>Stochastic environmental research and risk assessment >Multifractal properties of the peak flow distribution on stochastic drainage networks
【24h】

Multifractal properties of the peak flow distribution on stochastic drainage networks

机译:随机排水网络峰值流量分布的多重分形特性

获取原文
获取原文并翻译 | 示例
           

摘要

In this paper, we examined the peak flow distribution on a realization of networks obtained with stochastic network models. Three network models including the uniform model, the Scheidegger model, and the Gibbsian model were utilized to generate networks. The network efficiency in terms of drainage time is highest on the Scheidegger model, whereas it is lowest on the uniform model. The Gibbsian model covers both depending on the parameter value of β. The magnitude of the peak flow at the outlet itself is higher on the Scheidegger model compared to the uniform model. However, the results indicate that the maximum peak flows can be observed not just at the outlet but also other parts of the mainstream. The results show that the peak flow distribution on each stochastic model has a common multifractal spectrum. The minimum value of α, which is obtained in the limit of a sufficiently large q, is equal to the fractal dimension of a single river. The multifractal properties clearly show the difference among three stochastic network models and how they are related. Moreover, the results imply that the multifractal properties can be utilized to estimate the value of β for a given drainage network.
机译:在本文中,我们检查了使用随机网络模型获得的网络实现中的峰值流量分布。利用包括统一模型,Scheidegger模型和Gibbsian模型在内的三个网络模型来生成网络。在排水时间方面,网络效率在Scheidegger模型中最高,而在均匀模型中最低。吉布斯模型根据β的参数值覆盖了两者。与统一模型相比,在Scheidegger模型上,出口本身的峰值流量幅度更高。但是,结果表明,不仅在出口处,而且在主流的其他部分都可以观察到最大峰值流量。结果表明,每个随机模型的峰值流量分布具有共同的多重分形谱。在一个足够大的q的极限中获得的α的最小值等于一条河流的分形维数。多重分形特性清楚地显示了三种随机网络模型之间的差异以及它们之间的关系。此外,结果暗示,可以利用多重分形特性来估算给定排水网络的β值。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号