干旱区生态环境知识资源中心(Arid): Reference evapotranspiration estimation in hyper-arid regions via D-vine copula based-quantile regression and comparison with empirical approaches and machine learning models

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DOI	10.1016/j.ejrh.2022.101259
	Reference evapotranspiration estimation in hyper-arid regions via D-vine copula based-quantile regression and comparison with empirical approaches and machine learning models
	Abdallah, Mohammed; Mohammadi, Babak; Zaroug, Modathir A. H.; Omer, Abubaker; Cheraghalizadeh, Majid; Eldow, Mohamed E. E.; Duan, Zheng
通讯作者	Mohammadi, B
来源期刊	JOURNAL OF HYDROLOGY-REGIONAL STUDIES
EISSN	2214-5818
出版年	2022
卷号	44
英文摘要	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 compared with Multivariate Linear quantile regression (MLQR), Bayesians Model Averaging quantile regression (BMAQR), Empirical Models (EMMs), and Classical Machine Learning (CML). Besides, the CML models including Random Forest (RF), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Extreme Gradient Boosting (XGBoost), and M5 Model Tree (M5Tree) were employed. New hydrological insights for the region: The original EMMs showed poor performance, which improved after calibration techniques. The DVQR, MLQR, and BMAQR models showed better performance than the calibrated EMMs. However, the DVQR model exhibited the highest accu-racy than the MLQR and BMAQR models over two study sites. The M5Tree, SVM, and XGBoost models perfumed better than ELM and RF models at both study sites. The DVQR and XGBoost models showed equivalent performance (R-2, NSE, and WIA > 0.99, MAE, and RMSE < 0.2) to the M5Tree and SVM models, but they had significantly more accuracy than the calibrated EMMs, MLQR, BMAQR, ELM, and RF models in two hyper-arid regions. Overall, the high dimensional DVQR model is recommended as a promising alternative technique for estimating daily ETo in hyper-arid climate conditions around the world.
英文关键词	Reference evapotranspiration Empirical models Quantile regression Machine learning Sudan Hyper -arid region
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000891433200002
WOS关键词	LIMITED CLIMATIC DATA ; CROP EVAPOTRANSPIRATION ; METEOROLOGICAL DATA ; RANDOM FORESTS ; PREDICTION ; EQUATIONS ; SVM ; COMBINATION ; NORTHWEST ; ACCURACY
WOS类目	Water Resources
WOS研究方向	Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393530
推荐引用方式 GB/T 7714	Abdallah, Mohammed,Mohammadi, Babak,Zaroug, Modathir A. H.,et al. Reference evapotranspiration estimation in hyper-arid regions via D-vine copula based-quantile regression and comparison with empirical approaches and machine learning models[J],2022,44.
APA	Abdallah, Mohammed.,Mohammadi, Babak.,Zaroug, Modathir A. H..,Omer, Abubaker.,Cheraghalizadeh, Majid.,...&Duan, Zheng.(2022).Reference evapotranspiration estimation in hyper-arid regions via D-vine copula based-quantile regression and comparison with empirical approaches and machine learning models.JOURNAL OF HYDROLOGY-REGIONAL STUDIES,44.
MLA	Abdallah, Mohammed,et al."Reference evapotranspiration estimation in hyper-arid regions via D-vine copula based-quantile regression and comparison with empirical approaches and machine learning models".JOURNAL OF HYDROLOGY-REGIONAL STUDIES 44(2022).