干旱区生态环境知识资源中心(Arid): High-resolution reference evapotranspiration for arid Egypt: Comparative analysis and evaluation of empirical and artificial intelligence models

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DOI	10.1002/joc.7894
	High-resolution reference evapotranspiration for arid Egypt: Comparative analysis and evaluation of empirical and artificial intelligence models
	Sobh, Mohamed Tarek; Nashwan, Mohamed Salem; Amer, Nabil
通讯作者	Sobh, MT
来源期刊	INTERNATIONAL JOURNAL OF CLIMATOLOGY
ISSN	0899-8418
EISSN	1097-0088
出版年	2022
卷号	42 期号:16 页码:10217-10237
英文摘要	Accurate estimation of evapotranspiration has crucial importance in arid regions like Egypt, which suffers from the scarcity of precipitation and water shortages. This study provides an investigation of the performance of 31 widely used empirical equations and 20 models developed using five artificial intelligence (AI) algorithms to estimate reference evapotranspiration (ET0) to generate gridded high-resolution daily ET0 estimates over Egypt. The AI algorithms include support vector machine-radial basis function (SVM-RBF), random forest (RF), group method of data handling neural network (GMDH-NN), multivariate adaptive regression splines (MARS), and dynamic evolving neural fuzzy interference system (DENFIS). Daily observations records of 41 stations distributed over Egypt were used to calculate ET0 using FAO56 Penman-Monteith equation as a reference estimate. The multiparameter Kling-Gupta efficiency (KGE) metric was used as an evaluation metric for its robustness in representing different statistical error/agreement characteristics in a single value. By category, the empirical equations based on radiation performed better in replicating FAO56-PM followed by temperature- and mass-transfer-based ones. Ritchie equation was found to be the best overall in Egypt (median KGE 0.76) followed by Caprio (median KGE 0.64), and Penman (median KGE 0.52) equations based on station-wise ranking. On the other hand, the RF model, having maximum and minimum temperatures, wind speed, and relative humidity as predictors, outperformed other AI algorithms. Overall, the RF model performed the best among all the AI models and empirical equations. The generated 0.10 degrees x 0.10 degrees daily estimates of ET0 enabled the detection of a significant increase of 0.12-0.16 mm center dot decade(-1) in the agricultural-dependent Nile Delta using the modified Mann-Kendall test and Sen's slope estimator.
英文关键词	machine learning MENA Penman-Monteith equation potential evapotranspiration
类型	Article
语种	英语
开放获取类型	Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000876769300001
WOS关键词	FUZZY INFERENCE SYSTEM ; CLIMATE-CHANGE IMPACTS ; PRECIPITATION DATA ; EVAPORATION ; TEMPERATURE ; COEFFICIENT ; HARGREAVES ; EQUATIONS ; RAINFALL ; REGION
WOS类目	Meteorology & Atmospheric Sciences
WOS研究方向	Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393137
推荐引用方式 GB/T 7714	Sobh, Mohamed Tarek,Nashwan, Mohamed Salem,Amer, Nabil. High-resolution reference evapotranspiration for arid Egypt: Comparative analysis and evaluation of empirical and artificial intelligence models[J],2022,42(16):10217-10237.
APA	Sobh, Mohamed Tarek,Nashwan, Mohamed Salem,&Amer, Nabil.(2022).High-resolution reference evapotranspiration for arid Egypt: Comparative analysis and evaluation of empirical and artificial intelligence models.INTERNATIONAL JOURNAL OF CLIMATOLOGY,42(16),10217-10237.
MLA	Sobh, Mohamed Tarek,et al."High-resolution reference evapotranspiration for arid Egypt: Comparative analysis and evaluation of empirical and artificial intelligence models".INTERNATIONAL JOURNAL OF CLIMATOLOGY 42.16(2022):10217-10237.