干旱区生态环境知识资源中心(Arid): Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation

Arid

DOI	10.5194/hess-25-603-2021
	Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation
	Sattari, Mohammad Taghi; Apaydin, Halit; Band, Shahab S.; Mosavi, Amir; Prasad, Ramendra
通讯作者	Sattari, MT (corresponding author), Univ Tabriz, Fac Agr, Dept Water Engn, Tabriz 51666, Iran. ; Sattari, MT (corresponding author), Duy Tan Univ, Inst Res & Dev, Danang 550000, Vietnam. ; Sattari, MT (corresponding author), Ankara Univ, Fac Agr, Dept Agr Engn, TR-06110 Ankara, Turkey. ; Band, SS (corresponding author), Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu 64002, Yunlin, Taiwan.
来源期刊	HYDROLOGY AND EARTH SYSTEM SCIENCES
ISSN	1027-5606
EISSN	1607-7938
出版年	2021
卷号	25 期号:2 页码:603-618
英文摘要	Timely and accurate estimation of reference evapotranspiration (ET0) is indispensable for agricultural water management for efficient water use. This study aims to estimate the amount of ET0 with machine learning approaches by using minimum meteorological parameters in the Corum region, which has an arid and semi-arid climate and is regarded as an important agricultural centre of Turkey. In this context, monthly averages of meteorological variables, i.e. maximum and minimum temperature; sunshine duration; wind speed; and average, maximum, and minimum relative humidity, are used as inputs. Two different kemel-based methods, i.e. Gaussian process regression (GPR) and support vector regression (SVR), together with a Broyden- Fletcher-Goldfarb-Shanno artificial neural network (BFGSANN) and long short-term memory (LSTM) models were used to estimate ET0 amounts in 10 different combinations. The results showed that all four methods predicted ET0 amounts with acceptable accuracy and error levels. The BFGS-ANN model showed higher success (R-2 = 0.9781) than the others. In kernel-based GPR and SVR methods, the Pearson VII function-based universal kernel was the most successful (R-2 = 0.9771). Scenario 5, with temperatures including average temperature, maximum and minimum temperature, and sunshine duration as inputs, gave the best results. The second best scenario had only the sunshine duration as the input to the BFGS-ANN, which estimated ET0 having a correlation coefficient of 0.971 (Scenario 8). Conclusively, this study shows the better efficacy of the BFGS in ANNs for enhanced performance of the ANN model in ET0 estimation for drought-prone arid and semi-arid regions.
类型	Article
语种	英语
开放获取类型	Green Submitted, gold
收录类别	SCI-E
WOS记录号	WOS:000618978900002
WOS关键词	NEURAL-NETWORKS ; MODEL TREES ; OPTIMIZATION ; PREDICTION ; REGION ; PERFORMANCE ; VARIABLES ; SELECTION ; MACHINES ; CLIMATE
WOS类目	Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350514
作者单位	[Sattari, Mohammad Taghi] Univ Tabriz, Fac Agr, Dept Water Engn, Tabriz 51666, Iran; [Sattari, Mohammad Taghi] Duy Tan Univ, Inst Res & Dev, Danang 550000, Vietnam; [Sattari, Mohammad Taghi; Apaydin, Halit] Ankara Univ, Fac Agr, Dept Agr Engn, TR-06110 Ankara, Turkey; [Band, Shahab S.] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu 64002, Yunlin, Taiwan; [Mosavi, Amir] Tech Univ Dresden, Fac Civil Engn, D-01069 Dresden, Germany; [Mosavi, Amir] Obuda Univ, John von Neumann Fac Informat, H-1034 Budapest, Hungary; [Mosavi, Amir] Norwegian Univ Life Sci, Sch Econ & Business, N-1430 As, Norway; [Prasad, Ramendra] Univ Fiji, Sch Sci & Technol, Dept Sci, Lautoka, Fiji
推荐引用方式 GB/T 7714	Sattari, Mohammad Taghi,Apaydin, Halit,Band, Shahab S.,et al. Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation[J],2021,25(2):603-618.
APA	Sattari, Mohammad Taghi,Apaydin, Halit,Band, Shahab S.,Mosavi, Amir,&Prasad, Ramendra.(2021).Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation.HYDROLOGY AND EARTH SYSTEM SCIENCES,25(2),603-618.
MLA	Sattari, Mohammad Taghi,et al."Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation".HYDROLOGY AND EARTH SYSTEM SCIENCES 25.2(2021):603-618.