Dependence structure analysis of multisite river inflow data using vine copula-CEEMDAN based hybrid model.

ABSTRACT: Several data-driven and hybrid models are univariate and not considered the dependance structure of multivariate random variables, especially the multi-site river inflow data, which requires the joint distribution of the same river basin system. In this paper, we proposed a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Vine copula-based approach to address this issue. The proposed hybrid model comprised on two stages: In the first stage, the CEEMDAN is used to extract the high dimensional multi-scale features. Further, the multiple models are used to predict multi-scale components and residuals. In the second stage, the residuals obtained from the first stage are used to model the joint uncertainty of multi-site river inflow data by using Canonical Vine. For

SUBMITTER: Nazir HM 

PROVIDER: S-EPMC7651478 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature