Reference evapotranspiration estimation in hyper-arid regions via D-vine copula based-quantile regression and comparison with empirical approaches and machine learning models

Reference evapotranspiration estimation in hyper-arid regions via D-vine copula based-quantile regression and comparison with empirical approaches and machine learning models

Study region: Two hyper-arid regions (Atbara and Kassala stations) in Sudan. Study focus: The study aims to evaluate the potential of the D-vine Copula-based quantile regression (DVQR) model for estimating daily ETo during 2000–2015 based on various input structures. Further, the DVQR model was comp...

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Bibliographic Details
Main Authors: Mohammed Abdallah, Babak Mohammadi, Modathir A. H. Zaroug, Abubaker Omer, Majid Cheraghalizadeh, Mohamed E.E. Eldow, Zheng Duan
Format: Article
Language:English
Published: Elsevier 2022-12-01
Series:Journal of Hydrology: Regional Studies
Subjects:
Reference evapotranspiration
Empirical models
Quantile regression
Machine learning
Sudan
Hyper-arid region
Online Access:http://www.sciencedirect.com/science/article/pii/S2214581822002725

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http://www.sciencedirect.com/science/article/pii/S2214581822002725

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