Vine copula had a great impact on the study and analysis of dependence structures in various sciences. In multivariate analyses with dimensions of more than two variables, it is associated with computational complexities that solve vine copulas and these problems. In this study, in order to provide an approach to simulate potential evapotranspiration based on meteorological parameters in Birjand meteorological station from different family copulas including R-vine, independent R-vine, Gaussian, independent Gaussian, C-vine, C-vine independent, D-vine and D-vine independent were used. In this regard, vine copula simulation and conditional density were used. In pair correlation analysis of the studied variables using Kendall's tau statistic, dependence structure confirmed the studied parameters. The results showed a minimum correlation of −0.32 and a maximum of 0.77. The results of Akaike's information criteria (AIC), Bayesian information criteria (BIC) and LogLike statistics in evaluating the performance of vine copula dependency structure introduced the C-vine copula as the superior copula for analysing the pair dependence of the studied variables. By introducing the superior dependency structure and internal copulas, the tree sequence of the pair of values under study was obtained. Pair of simulated values was performed using vine copula. Comparison of Kendall's tau values in both simulation and observation modes showed that Kendall's tau values were close to each other in both modes and were approximately similar. The simulation results of vine copula potential evapotranspiration values and precipitation, temperature and relative humidity values showed 92% efficiency. The efficiency of C-vine copula in dependence analysis and simulation of potential evapotranspiration (PET) values is very high, which shows the ability of vine family copulas in multivariate analysis.

中文翻译：

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基于葡萄系copula的潜在蒸散值模拟

Vine copula 对各种科学中依赖结构的研究和分析产生了很大的影响。在具有两个以上变量维度的多变量分析中，它与解决藤蔓 copula 和这些问题的计算复杂性有关。在本研究中，为了提供一种基于 Birjand 气象站气象参数模拟潜在蒸散量的方法，包括 R-vine、独立 R-vine、高斯、独立高斯、C-vine、C-vine 独立，使用 D-vine 和 D-vine 独立。在这方面，使用了藤蔓 copula 模拟和条件密度。在使用 Kendall tau 统计量对研究变量的配对相关分析中，依赖结构证实了研究参数。结果显示最小相关性为 -0。32 和最大值 0.77。Akaike的信息准则(AIC)、贝叶斯信息准则(BIC)和LogLike统计在评价葡萄系copula依赖结构的性能方面的结果引入了C-vine copula作为分析研究变量对依赖的上级copula。通过引入上级依赖结构和内部copula，得到了所研究值对的树序列。使用 vine copula 执行一对模拟值。比较模拟和观察模式下的 Kendall tau 值表明，Kendall tau 值在两种模式下彼此接近并且大致相似。藤蔓 copula 潜在蒸散值和降水、温度和相对湿度值的模拟结果显示效率为 92%。