干旱区生态环境知识资源中心(Arid): Evaluation of multivariate linear regression for reference evapotranspiration modeling in different climates of Iran

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DOI	10.1007/s00704-020-03473-0
	Evaluation of multivariate linear regression for reference evapotranspiration modeling in different climates of Iran
	Sharafi, Saeed; Ghaleni, Mehdi Mohammadi
通讯作者	Sharafi, S (corresponding author), Arak Univ, Dept Environm Sci & Engn, Arak, Iran.
来源期刊	THEORETICAL AND APPLIED CLIMATOLOGY
ISSN	0177-798X
EISSN	1434-4483
出版年	2021
卷号	143 期号:3-4 页码:1409-1423
英文摘要	The study aimed to evaluate the accuracy of empirical equations (Hargreaves-Samani; HS, Irmak; IR and Dalton; DT) and multivariate linear regression models (MLR1-6) for estimating reference evapotranspiration (ETRef) in different climates of Iran based on the Koppen method including arid desert (Bw), semiarid (Bs), humid with mild winters (C), and humid with severe winters (D). For this purpose, climatic data of 33 meteorological stations during 30 statistical years 1990-2019 were used with a monthly time step. Based on various meteorological data (minimum and maximum temperature, relative humidity, wind speed, solar radiation, extraterrestrial radiation, and vapor pressure deficit), in addition to 6 multivariate linear regression models and three empirical equations were used as MLR1, MLR2, and HS (temperature-based), MLR3 and IR (radiation-based), MLR4, MLR5 and DT (mass transfer-based), and MLR6 (combination-based) were also used to estimate the reference evapotranspiration. The results of these models were compared using the root mean square error (RMSE), mean absolute error (MAE), scatter index (SI), determination coefficient (R-2), and Nash-Sutcliffe efficiency (NSE) statistical criteria with the evapotranspiration results of the FAO(56) Penman-Monteith reference as target data. All MLR models gave better results than empirical equations. The results showed that the simplest regression model (MLR1) based on the minimum and maximum temperature data was more accurate than the empirical equations. The lowest and highest accuracy related to the MLR6 model and HS empirical equation with RMSE was 10.8-15.1 mm month(-1) and 22-28.3 mm month(-1), respectively. Also, among all the evaluated equations, radiation-based models such as IR in Bw and Bs climates with MAE = 8.01-11.2 mm month(-1) had higher accuracy than C and D climates with MAE = 13.44-14.48 mm month(-1). In general, the results showed that the ability of regression models was excellent in all climates from Bw to D based on SI < 0.2.
类型	Article
语种	英语
开放获取类型	Other Gold
收录类别	SCI-E
WOS记录号	WOS:000605879100012
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; PREDICTION ; EQUATIONS ; RADIATION ; SOLAR ; SVM ; GEP
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/2XILL650/348246
作者单位	[Sharafi, Saeed] Arak Univ, Dept Environm Sci & Engn, Arak, Iran; [Ghaleni, Mehdi Mohammadi] Arak Univ, Dept Water Sci & Engn, Arak, Iran
推荐引用方式 GB/T 7714	Sharafi, Saeed,Ghaleni, Mehdi Mohammadi. Evaluation of multivariate linear regression for reference evapotranspiration modeling in different climates of Iran[J],2021,143(3-4):1409-1423.
APA	Sharafi, Saeed,&Ghaleni, Mehdi Mohammadi.(2021).Evaluation of multivariate linear regression for reference evapotranspiration modeling in different climates of Iran.THEORETICAL AND APPLIED CLIMATOLOGY,143(3-4),1409-1423.
MLA	Sharafi, Saeed,et al."Evaluation of multivariate linear regression for reference evapotranspiration modeling in different climates of Iran".THEORETICAL AND APPLIED CLIMATOLOGY 143.3-4(2021):1409-1423.