Hydrological dependent structure modeling method based on mutual information and vine copula

Abstract

The invention discloses a hydrological dependent structure modeling method based on mutual information and vine copula. Firstly, mutual information and conditional mutual information are used to measure the correlation and uncertainty of hydrological variables, in combination with the principle of the strongest correlation and the least uncertainty, the structure of vine copula is selected, starting from the first tree, the mutual information of pairs of paired variables is calculated, the pairing mode that maximizes the sum of mutual information is selected as the edge of the tree, the conditional mutual information of possible pairing variables is calculated, and the pairing mode that maximizes the sum of conditional mutual information is selected as tree 2, and the pairing mode is repeated until the structure of the whole tree is determined. Secondly, according to the tree structure, fitting of edge distribution is carried out, a goodness-of-fit test is carried out, starting from tree 1, the AIC criterion is utilized to determine the copula type of the edge, parameters are estimated, a goodness-of-fit test is performed, then the conditional edge distribution of variables is calculated, and the determination of copula type, estimating parameters and testing steps are repeated until all the trees are determined. All trees and edges are connected to complete the modeling of hydrological dependent structures.

Description

technical field [0001] The invention belongs to a hydrological interdependence structure modeling method, in particular to a hydrological interdependence structural modeling method based on mutual information and vine copula. Background technique [0002] Hydrological events are characterized by multiple variables. For example, floods are characterized by three hydrological variables: flood peak, duration, and total flood volume; droughts are characterized by three variables: drought intensity, drought duration, and drought peak. Therefore, hydrological risk analysis and management Multivariate correlations need to be analyzed and the interdependence structure of hydrological variables modeled. Vine-copulas have been increasingly used in multivariate models, where the structural choice of vine-copula plays a key role in modeling multivariate dependent structures. There are a lot of uncertainties in the hydrological system. In the modeling of the hydrological interdependence...

AI Technical Summary

Problems solved by technology

Based on the p value, the choice of degrees of freedom and AIC needs to estimate the copula in advance, and the process is cumbersome

[0005] At present, there is no application of mutual information and conditional mutual information to the hydrological interdependence structure modeling method based on mutual information and vine copula, and the traditional method cannot simultaneously consider the correlation and uncertainty of variables when modeling hydrological interdependence structures. The hydrological interdependence structure modeling method based on mutual information and copula can realize the simultaneous consideration of correlation and uncertainty

Images