干旱区生态环境知识资源中心(Arid): Comparison of different empirical methods and data-driven models for estimating reference evapotranspiration in semi-arid Central Anatolian Region of Turkey

Arid

DOI	10.1007/s12517-021-08150-8
	Comparison of different empirical methods and data-driven models for estimating reference evapotranspiration in semi-arid Central Anatolian Region of Turkey
	Yurtseven, Ibrahim; Serengil, Yusuf
通讯作者	Yurtseven, I (corresponding author)，Istanbul Univ Cerrahpasa, Fac Forestry, Dept Watershed Management, TR-34473 Istanbul, Turkey.
来源期刊	ARABIAN JOURNAL OF GEOSCIENCES
ISSN	1866-7511
EISSN	1866-7538
出版年	2021
卷号	14 期号:19
英文摘要	Evapotranspiration (ET) is a major hydrologic process to assess water budgets in terrestrial ecosystems. Since measurement of ET may involve labor intensive field technics in large areas, estimation is preferred in most cases. The FAO Penman-Monteith (PM FAO-56) is a widely recognized reference evapotranspiration (ETo) method for potential evapotranspiration calculations. The method requires a detailed and comprehensive meteorological data set; however, some empirical methods and models have attempted to calculate ET with less data. In this study, Makkink (ET_Mak), Hargreaves-Samani (ET_Har), Thornthwaite (ET_Thor), Blaney-Criddle (ET_BC), and Penman (ET_PM) were tested against the PM FAO-56. Penman method has achieved the highest accuracy among the empirical methods. In addition, the potential of artificial neural networks (ANN), support vector machines (SVM), random forest (RF), and multiple linear regression (MLR) for estimating ETo were investigated in a semi-arid Central Anatolian Region of Turkey. The results obtained with the ANN (based on multi-layer perceptron) and SVM models performed better than other tested data-driven models and empirical methods. These models could be used most effectively at elevation range of 850-1000 m. According to our results MLP, SVM, and Penman methods provided good performances in semi-arid regions in agricultural planning and water resources management studies. Furthermore, we concluded that integrating maximum temperature may result in improved accuracy in ET model simulations in semi-arid regions.
英文关键词	Reference evapotranspiration (ETo) Empirical methods Artificial neural networks (ANN) Support vector machines (SVM) Random forest (RF) Multiple linear regression (MLR)
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000746072400001
WOS关键词	LIMITED CLIMATIC DATA ; ESTIMATING POTENTIAL EVAPOTRANSPIRATION ; ARTIFICIAL NEURAL-NETWORK ; ELEVATION GRADIENT ; EVAPORATION ; REGRESSION ; TEMPERATURE ; PERFORMANCE ; CALIBRATION ; FORECAST
WOS类目	Geosciences, Multidisciplinary
WOS研究方向	Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/375904
作者单位	[Yurtseven, Ibrahim; Serengil, Yusuf] Istanbul Univ Cerrahpasa, Fac Forestry, Dept Watershed Management, TR-34473 Istanbul, Turkey
推荐引用方式 GB/T 7714	Yurtseven, Ibrahim,Serengil, Yusuf. Comparison of different empirical methods and data-driven models for estimating reference evapotranspiration in semi-arid Central Anatolian Region of Turkey[J],2021,14(19).
APA	Yurtseven, Ibrahim,&Serengil, Yusuf.(2021).Comparison of different empirical methods and data-driven models for estimating reference evapotranspiration in semi-arid Central Anatolian Region of Turkey.ARABIAN JOURNAL OF GEOSCIENCES,14(19).
MLA	Yurtseven, Ibrahim,et al."Comparison of different empirical methods and data-driven models for estimating reference evapotranspiration in semi-arid Central Anatolian Region of Turkey".ARABIAN JOURNAL OF GEOSCIENCES 14.19(2021).