干旱区生态环境知识资源中心(Arid): Estimating evapotranspiration by coupling Bayesian model averaging methods with machine learning algorithms

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DOI	10.1007/s10661-021-08934-1
	Estimating evapotranspiration by coupling Bayesian model averaging methods with machine learning algorithms
	Yang, Yong; Sun, Huaiwei; Xue, Jie; Liu, Yi; Liu, Luguang; Yan, Dong; Gui, Dongwei
通讯作者	Sun, HW (corresponding author), Huazhong Univ Sci & Technol, Sch Civil & Hydraul Engn, Wuhan 430074, Peoples R China.
来源期刊	ENVIRONMENTAL MONITORING AND ASSESSMENT
ISSN	0167-6369
EISSN	1573-2959
出版年	2021
卷号	193 期号:3
英文摘要	Evapotranspiration (ET) is one of the most important components of global hydrologic cycle and has significant impacts on energy exchange and climate change. Numerous models have been developed to estimate ET so far; however, great uncertainties in models still require considerations. The aim of this study is to reduce model errors and uncertainties among multi-models to improve daily ET estimate. The Bayesian model averaging (BMA) method is used to assemble eight ET models to produce ET with Landsat 8 satellite data, including four surface energy balance models (i.e., SEBS, SEBAL, SEBI, and SSEB) and four machine learning algorithms (i.e., polymars, random forest, ridge regression, and support vector machine). Performances of each model and BMA method were validated through in situ measurements of semi-arid region. Results indicated that the BMA method outperformed all eight single models. The four most important models obtained by the BMA method were ranked by random forest, SVM, SEBS, and SEBAL. The BMA method coupled with machine learning can significantly improve the accuracy of daily ET estimate, reducing uncertainties among models, and taking different intrinsic benefits of empirically and physically based models to obtain a more reliable ET estimate.
英文关键词	Evapotranspiration Surface energy balance Bayesian model averaging (BMA) Machine learning Landsat
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000624535100001
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
来源机构	中国科学院新疆生态与地理研究所
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350130
作者单位	[Yang, Yong; Sun, Huaiwei; Yan, Dong] Huazhong Univ Sci & Technol, Sch Civil & Hydraul Engn, Wuhan 430074, Peoples R China; [Xue, Jie; Liu, Yi; Gui, Dongwei] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China; [Liu, Luguang] Hubei Water Resources Res Inst, Wuhan 430072, Peoples R China
推荐引用方式 GB/T 7714	Yang, Yong,Sun, Huaiwei,Xue, Jie,et al. Estimating evapotranspiration by coupling Bayesian model averaging methods with machine learning algorithms[J]. 中国科学院新疆生态与地理研究所,2021,193(3).
APA	Yang, Yong.,Sun, Huaiwei.,Xue, Jie.,Liu, Yi.,Liu, Luguang.,...&Gui, Dongwei.(2021).Estimating evapotranspiration by coupling Bayesian model averaging methods with machine learning algorithms.ENVIRONMENTAL MONITORING AND ASSESSMENT,193(3).
MLA	Yang, Yong,et al."Estimating evapotranspiration by coupling Bayesian model averaging methods with machine learning algorithms".ENVIRONMENTAL MONITORING AND ASSESSMENT 193.3(2021).