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	Future Projection with an Extreme-Learning Machine and Support Vector Regression of Reference Evapotranspiration in a Mountainous Inland Watershed in North-West China
	Yin, Zhenliang1; Feng, Qi1; Yang, Linshan1; Deo, Ravinesh C.1,2; Wen, Xiaohu1; Si, Jianhua1; Xiao, Shengchun1
刊名	WATER
	2017-11-01
卷号	9期号:11页码:23
关键词	reference evapotranspiration (ET0) extreme-learning machine support vector regression ET0 projection climate change
ISSN号	2073-4441
DOI	10.3390/w9110880
通讯作者	Feng, Qi(qifeng@lzb.ac.cn) ; Deo, Ravinesh C.(Ravinesh.Deo@usq.edu.au)
英文摘要	This study aims to project future variability of reference evapotranspiration (ET0) using artificial intelligence methods, constructed with an extreme-learning machine (ELM) and support vector regression (SVR) in a mountainous inland watershed in north-west China. Eight global climate model (GCM) outputs retrieved from the Coupled Model Inter-comparison Project Phase 5 (CMIP5) were employed to downscale monthly ET0 for the historical period 1960-2005 as a validation approach and for the future period 2010-2099 as a projection of ET0 under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. The following conclusions can be drawn: the ELM and SVR methods demonstrate a very good performance in estimating Food and Agriculture Organization (FAO)-56 Penman-Monteith ET0. Variation in future ET0 mainly occurs in the spring and autumn seasons, while the summer and winter ET0 changes are moderately small. Annually, the ET0 values were shown to increase at a rate of approximately 7.5 mm, 7.5 mm, 0.0 mm (8.2 mm, 15.0 mm, 15.0 mm) decade(-1), respectively, for the near-term projection (2010-2039), mid-term projection (2040-2069), and long-term projection (2070-2099) under the RCP4.5 (RCP8.5) scenario. Compared to the historical period, the relative changes in ET0 were found to be approximately 2%, 5% and 6% (2%, 7% and 13%), during the near, mid- and long-term periods, respectively, under the RCP4.5 (RCP8.5) warming scenarios. In accordance with the analyses, we aver that the opportunity to downscale monthly ET0 with artificial intelligence is useful in practice for water-management policies.
收录类别	SCI
WOS关键词	REFERENCE CROP EVAPOTRANSPIRATION ; ARTIFICIAL NEURAL-NETWORKS ; LIMITED CLIMATIC DATA ; RIVER-BASIN ; POTENTIAL EVAPOTRANSPIRATION ; LOESS PLATEAU ; STANDARDIZED PRECIPITATION ; GENETIC ALGORITHM ; EASTERN AUSTRALIA ; SEMIARID REGION
WOS研究方向	Water Resources
WOS类目	Water Resources
语种	英语
出版者	MDPI AG
WOS记录号	WOS:000416798300066
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2558050
专题	寒区旱区环境与工程研究所
通讯作者	Feng, Qi; Deo, Ravinesh C.
作者单位	1.Chinese Acad Sci, Northwest Inst Ecoenviron & Resources, Key Lab Ecohydrol Inland River Basin, Lanzhou 730000, Gansu, Peoples R China 2.Univ Southern Queensland, Inst Agr & Environm IAg&E, Sch Agr Computat & Environm Sci, Springfield, Qld 4300, Australia
推荐引用方式 GB/T 7714	Yin, Zhenliang,Feng, Qi,Yang, Linshan,et al. Future Projection with an Extreme-Learning Machine and Support Vector Regression of Reference Evapotranspiration in a Mountainous Inland Watershed in North-West China[J]. WATER,2017,9(11):23.
APA	Yin, Zhenliang.,Feng, Qi.,Yang, Linshan.,Deo, Ravinesh C..,Wen, Xiaohu.,...&Xiao, Shengchun.(2017).Future Projection with an Extreme-Learning Machine and Support Vector Regression of Reference Evapotranspiration in a Mountainous Inland Watershed in North-West China.WATER,9(11),23.
MLA	Yin, Zhenliang,et al."Future Projection with an Extreme-Learning Machine and Support Vector Regression of Reference Evapotranspiration in a Mountainous Inland Watershed in North-West China".WATER 9.11(2017):23.

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