Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210023, PR China.
Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing, 210023, PR China.
Environ Res. 2020 Jul;186:109604. doi: 10.1016/j.envres.2020.109604. Epub 2020 Apr 28.
Hydrological risk analysis and management entails multivariate modeling which requires modeling the structure of dependence among different variables. Vine copulas have been increasing applied in multivariate modeling wherein the selection of vine copula structure plays a critical role. Inspired by the relationship between Mutual information (MI) and copula entropy (CE), this study discussed the connection between conditional mutual information (CMI) and CE and developed a mutual information-based sequential approach to select a vine structure which was based on original observations, and model-independent. Then, to reduce the complexity of R-vine copulas, a statistical method-based truncation procedure was applied. Finally, an MI-based approach for hydrological dependence modeling was developed. Two types of hydrological processes with different dependence structures were utilized to show the performance of the proposed approach: (i) drought characterization: showing a D-vine structure; and (ii) multi-site streamflow dependence: showing a C-vine structure. Results indicated that the MI-based approach satisfactorily modeled different kinds of dependence structure and yielded more information on variables in comparison with traditional tau-based approach.
水文风险分析和管理需要进行多元建模，这需要对不同变量之间的依赖结构进行建模。Vine Copulas 在多元建模中得到了越来越多的应用，其中 Vine Copula 结构的选择起着关键作用。受互信息 (MI) 和 Copula 熵 (CE) 之间关系的启发，本研究讨论了条件互信息 (CMI) 和 CE 之间的联系，并开发了一种基于互信息的序列方法来选择基于原始观测值的 Vine 结构，且该方法与模型无关。然后，为了降低 R-vine Copulas 的复杂性，应用了一种基于统计方法的截断程序。最后，开发了一种基于互信息的水文依赖建模方法。利用具有不同依赖结构的两种类型的水文过程来展示所提出方法的性能：(i)干旱特征描述：显示 D-vine 结构；和 (ii)多站点流量依赖：显示 C-vine 结构。结果表明，与传统的基于 tau 的方法相比，基于互信息的方法可以很好地模拟不同类型的依赖结构，并为变量提供更多信息。
