气候变化领域集成服务门户(CCPortal): Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China

CCPortal

DOI	10.1007/s11269-017-1811-6
	Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China
	Yu, Haijiao; Wen, Xiaohu; Feng, Qi; Deo, Ravinesh C.; Si, Jianhua; Wu, Min
发表日期	2018
ISSN	0920-4741
EISSN	1573-1650
卷号	32 期号:1
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.
关键词	Discrete wavelet transform Artificial neural network Support vector regression Groundwater level fluctuations Extreme arid regions
学科领域	Engineering; Water Resources
语种	英语
WOS研究方向	Engineering, Civil ; Water Resources
来源期刊	WATER RESOURCES MANAGEMENT
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/112012
作者单位	Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Ecohydrol Inland River Basin, Lanzhou 730000, Gansu, Peoples R China
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]. 中国科学院西北生态环境资源研究院,2018,32(1).
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).
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).