干旱区生态环境知识资源中心(Arid): A novel integrated method based on a machine learning model for estimating evapotranspiration in dryland

Arid

DOI	10.1016/j.jhydrol.2021.126881
	A novel integrated method based on a machine learning model for estimating evapotranspiration in dryland
	Fu, Tonglin; Li, Xinrong; Jia, Rongliang; Feng, Li
通讯作者	Li, XR (corresponding author), Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Shapotou Desert Res & Expt Stn, Lanzhou, Peoples R China.
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2021
卷号	603
英文摘要	Evapotranspiration (ET) plays a vital role in the water cycle and energy cycle and serves as an important linkage between ecological and hydrological processes. Accurate estimation of ET based on data-driven methods is of great theoretical and practical significance for exploring soil evaporation, plant transpiration and the regional hydrological balance. Most existing estimation approaches were proposed based on multiple meteorological variables. This study proposed a novel hybrid estimation approach to estimate the monthly ET using only historical ET time series in the southeastern margins of the Tengger Desert, China. The approach consisted of three sections including data preprocessing, parameter optimization and estimation. The model evaluation demonstrated that the hybrid model based on the variational mode decomposition (VMD) method, grey wolf optimizer (GWO) algorithm and support vector machine (SVM) model achieved superior computational performance compared to the performance of other methods. The Nash-Sutcliffe coefficient of efficiency (NSCE) increased from 0.8588 to 0.8754 and the mean absolute percentage error (MAPE) decreased from 28.42% to 23.22% in the testing stage. Thus, we suggest that the hybrid VMD-GWO-SVM model will be the best choice for estimating ET in the absence of regional meteorological monitoring.
英文关键词	Evapotranspiration Variational mode decomposition Grey wolf optimizer algorithm Support vector machine Tengger Desert
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000706313000065
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; SUPPORT-VECTOR-MACHINE ; DECOMPOSITION ; EVAPORATION ; ALGORITHM ; REGRESSION ; EQUATIONS ; MARS ; SVM ; ELM
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368139
作者单位	[Fu, Tonglin; Li, Xinrong; Jia, Rongliang; Feng, Li] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Shapotou Desert Res & Expt Stn, Lanzhou, Peoples R China; [Fu, Tonglin] Univ Chinese Acad Sci, Beijing, Peoples R China; [Fu, Tonglin] Longdong Univ, Sch Math & Stat, Qingyang, Peoples R China
推荐引用方式 GB/T 7714	Fu, Tonglin,Li, Xinrong,Jia, Rongliang,et al. A novel integrated method based on a machine learning model for estimating evapotranspiration in dryland[J],2021,603.
APA	Fu, Tonglin,Li, Xinrong,Jia, Rongliang,&Feng, Li.(2021).A novel integrated method based on a machine learning model for estimating evapotranspiration in dryland.JOURNAL OF HYDROLOGY,603.
MLA	Fu, Tonglin,et al."A novel integrated method based on a machine learning model for estimating evapotranspiration in dryland".JOURNAL OF HYDROLOGY 603(2021).