论文标题
SUPCBI过程，并应用于流量排放和降低模型
A supCBI process with application to streamflow discharge and a model reduction
论文作者
论文摘要
我们提出了一个新的随机模型，用于流量排放时间表作为跳跃驱动的过程，称为连续状态分支过程的叠加，随着移民的（SUPCBI过程）。这是一个非马克维亚模型，具有重现水文数据中发现的亚指数自相关的能力。 Markovian嵌入作为矩阵分析方法的版本被应用于SupCBI过程，成功得出了统计矩和自相关的分析公式。在研究地点确定了SUPCBI过程，其中有小时流量流出数据。我们还将Markovian嵌入的另一种嵌入方式视为将SUPCBI过程的模型减少到连续的高流量和低流量方案的连续二进制半摩托车链。我们表明，可以使用指数分布的混合物对等待时间进行建模，这表明半马多夫链有效地减少了SUPCBI过程的模型。
We propose a new stochastic model for streamflow discharge timeseries as a jump-driven process, called a superposition of continuous-state branching processes with immigration (a supCBI process). It is a non-Markovian model having the capability of reproducing the subexponential autocorrelation found in the hydrological data. The Markovian embedding as a version of matrix analytic methods is applied to the supCBI process, successfully yielding analytical formulae of statistical moments and autocorrelation. The supCBI process is identified at study sites, where hourly streamflow discharge data are available. We also consider another Markovian embedding as a model reduction of the supCBI process to a continuous-time binary semi-Markov chain of high- and low-flow regimes. We show that waiting times can be modeled using a mixture of exponential distributions, suggesting that semi-Markov chains serve as effectively reduced models of the supCBI process.
