干旱区生态环境知识资源中心(Arid): Reference evapotranspiration estimation using machine learning approaches for arid and semi-arid regions of India

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DOI	10.3354/cr01723
	Reference evapotranspiration estimation using machine learning approaches for arid and semi-arid regions of India
	Heramb, Pangam; Rao, K. V.; Subeesh, A.; Singh, R. K.; Rajwade, Yogesh A.; Singh, Karan; Kumar, Manoj; Rawat, Shashi
通讯作者	Rajwade, YA
来源期刊	CLIMATE RESEARCH
ISSN	0936-577X
EISSN	1616-1572
出版年	2023
卷号	91 页码:97-120
英文摘要	Accurate estimation of reference evapotranspiration (ET0) is vital for hydrological studies and irrigation scheduling. This study aimed to estimate ET0 using 4 machine learning algorithms: random forest, support vector machine, light gradient boosting decision trees and extreme gradient decision trees. Daily data for 2001 to 2020 at 11 (arid and semi-arid) stations were used for modelling. Eighteen scenarios with different input combinations were evaluated using the data of maximum and minimum air temperature, mean relative humidity and wind speed, number of sunshine hours, solar radiation, and extra-terrestrial radiation data at these stations. The ET0 estimated using the FAO 56 Penman-Monteith equation was chosen as the target value for model fitting. The best input combination was found in the models that used all inputs, while the least accurate were the models that used temperature data only. The results showed that the support vector machine models outperformed the other models at most stations. The application of various input combinations indicated that the use of fewer inputs also gave reasonable accuracy in the modelling. In addition, wind speed and solar radiation were found to be important parameters for precise estimation.
英文关键词	Arid Semi-arid Evapotranspiration Soft computing Tree-based models
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001079837100001
WOS关键词	MODELS ; CLIMATES ; ELM
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395781
推荐引用方式 GB/T 7714	Heramb, Pangam,Rao, K. V.,Subeesh, A.,et al. Reference evapotranspiration estimation using machine learning approaches for arid and semi-arid regions of India[J],2023,91:97-120.
APA	Heramb, Pangam.,Rao, K. V..,Subeesh, A..,Singh, R. K..,Rajwade, Yogesh A..,...&Rawat, Shashi.(2023).Reference evapotranspiration estimation using machine learning approaches for arid and semi-arid regions of India.CLIMATE RESEARCH,91,97-120.
MLA	Heramb, Pangam,et al."Reference evapotranspiration estimation using machine learning approaches for arid and semi-arid regions of India".CLIMATE RESEARCH 91(2023):97-120.