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	Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China
	Yu, Haijiao1,2; Wen, Xiaohu1; Feng, Qi1; Deo, Ravinesh C.3; Si, Jianhua1; Wu, Min1,2
刊名	WATER RESOURCES MANAGEMENT
	2018
卷号	32期号:1页码:301-323
关键词	Discrete wavelet transform Artificial neural network Support vector regression Groundwater level fluctuations Extreme arid regions
ISSN号	0920-4741
DOI	10.1007/s11269-017-1811-6
通讯作者	Wen, Xiaohu(xhwen@lzb.ac.cn) ; Deo, Ravinesh C.(ravinesh.deo@usq.edu.au)
英文摘要	Prediction of groundwater depth (GWD) is a critical task in water resources management. In this study, the practicability of predicting GWD for lead times of 1, 2 and 3 months for 3 observation wells in the Ejina Basin using the wavelet-artificial neural network (WA-ANN) and wavelet-support vector regression (WA-SVR) is demonstrated. Discrete wavelet transform was applied to decompose groundwater depth and meteorological inputs into approximations and detail with predictive features embedded in high frequency and low frequency. WA-ANN and WA-SVR relative of ANN and SVR were evaluated with coefficient of correlation (R), Nash-Sutcliffe efficiency (NS), mean absolute error (MAE), and root mean squared error (RMSE). Results showed that WA-ANN and WA-SVR have better performance than ANN and SVR models. WA-SVR yielded better results than WA-ANN model for 1, 2 and 3-month lead times. The study indicates that WA-SVR could be applied for groundwater forecasting under ecological water conveyance conditions.
收录类别	SCI
WOS关键词	NEURAL-NETWORK APPROACH ; SUPPORT VECTOR MACHINE ; HEIHE RIVER ; LOWER REACHES ; EJINA BASIN ; TIME-SERIES ; VEGETATION ; LEVEL ; WATER ; FLUCTUATIONS
WOS研究方向	Engineering ; Water Resources
WOS类目	Engineering, Civil ; Water Resources
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000419553800018
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2558060
专题	寒区旱区环境与工程研究所
通讯作者	Wen, Xiaohu; Deo, Ravinesh C.
作者单位	1.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Ecohydrol Inland River Basin, Lanzhou 730000, Gansu, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.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	Yu, Haijiao,Wen, Xiaohu,Feng, Qi,et al. Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China[J]. WATER RESOURCES MANAGEMENT,2018,32(1):301-323.
APA	Yu, Haijiao,Wen, Xiaohu,Feng, Qi,Deo, Ravinesh C.,Si, Jianhua,&Wu, Min.(2018).Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China.WATER RESOURCES MANAGEMENT,32(1),301-323.
MLA	Yu, Haijiao,et al."Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China".WATER RESOURCES MANAGEMENT 32.1(2018):301-323.

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