Probabilistic modeling of flood characterizations with parametric and minimum information pair-copula model
Probabilistic modeling of flood characterizations with parametric and minimum information pair-copula model
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DOI：
10.1016/j.jhydrol.2016.06.044
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发表时间：
2016-09
影响因子：
6.4
通讯作者：
A. Daneshkhah;R. Remesan;O. Chatrabgoun;I. Holman
中科院分区：
文献类型：
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作者：
A. Daneshkhah;R. Remesan;O. Chatrabgoun;I. Holman
本文强调了最小信息和参数对耦合构造（PCC）对洪水事件属性联合分布建模的有效性。这两种模型在模拟多变量洪水数据方面都优于其他标准的多变量copula，这些数据显示出复杂的依赖模式，特别是在尾部。其中，最小信息对-联结模型具有较大的灵活性和较好的联合概率密度近似值，相应的措施具有有效的危害评估能力。研究表明，利用最小信息对耦合模型可以将任意多变量密度近似到所需精度的任意程度，并可实际用于概率洪水灾害评估。
This paper highlights the usefulness of the minimum information and parametric pair-copula construction (PCC) to model the joint distribution of flood event properties. Both of these models outperform other standard multivariate copula in modeling multivariate flood data that exhibiting complex patterns of dependence, particularly in the tails. In particular, the minimum information pair-copula model shows greater flexibility and produces better approximation of the joint probability density and corresponding measures have capability for effective hazard assessments. The study demonstrates that any multivariate density can be approximated to any degree of desired precision using minimum information pair-copula model and can be practically used for probabilistic flood hazard assessment.