Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China
文献类型：期刊论文
| 作者 | Yu, Haijiao1,2; Wen, Xiaohu1; Feng, Qi1; Deo, Ravinesh C.3; Si, Jianhua1; Wu, Min1,2 |
| 刊名 | WATER RESOURCES MANAGEMENT
 |
| 出版日期 | 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 |
| WOS记录号 | WOS:000419553800018 |
| 出版者 | SPRINGER |
| URI标识 | http://www.irgrid.ac.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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来源：寒区旱区环境与工程研究所
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