A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China

A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China

In this study, a C-vine copula-based quantile regression (CVQR) model is proposed for forecasting monthly streamflow. The CVQR model integrates techniques for vine copulas and quantile regression into a framework that can effectively establish relationships between the multidimensional response-inde...

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Bibliographic Details
Main Authors: Huawei Li, Guohe Huang, Yongping Li, Jie Sun, Pangpang Gao
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Sustainability
Subjects:
streamflow forecasting
C-vine copula
quantile regression
joint dependencies
water resource management
Online Access:https://www.mdpi.com/2071-1050/13/9/4627

Internet

https://www.mdpi.com/2071-1050/13/9/4627

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