干旱区生态环境知识资源中心(Arid): Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression

Arid

DOI	10.1080/19942060.2021.1944913
	Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression
	Band, Shahab S.; Heggy, Essam; Bateni, Sayed M.; Karami, Hojat; Rabiee, Mobina; Samadianfard, Saeed; Chau, Kwok-Wing; Mosavi, Amir
通讯作者	Band, SS (corresponding author), Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu, Yunlin, Taiwan. ; Mosavi, A (corresponding author), Tech Univ Dresden, Fac Civil Engn, Dresden, Germany. ; Mosavi, A (corresponding author), Obuda Univ, John von Neumann Fac Informat, Budapest, Hungary. ; Mosavi, A (corresponding author), J Selye Univ, Dept Informat, Komarno, Slovakia.
来源期刊	ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
ISSN	1994-2060
EISSN	1997-003X
出版年	2021
卷号	15 期号:1 页码:1147-1158
英文摘要	Utilizing new approaches to accurately predict groundwater level (GWL) in arid regions is of vital importance. In this study, support vector regression (SVR), Gaussian process regression (GPR), and their combination with wavelet transformation (named wavelet-support vector regression (W-SVR) and wavelet-Gaussian process regression (W-GPR)) are used to forecast groundwater level in Semnan plain (arid area) for the next month. Three different wavelet transformations, namely Haar, db4, and Symlet, are tested. Four statistical metrics, namely root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R (2)), and Nah-Sutcliffe efficiency (NS), are used to evaluate performance of different methods. The results reveal that SVR with RMSE of 0.04790 (m), MAPE of 0.00199%, R (2) of 0.99995, and NS of 0.99988 significantly outperforms GPR with RMSE of 0.55439 (m), MAPE of 0.04363%, R2 of 0.99264, and NS of 0.98413. Besides, the hybrid W-GPR-1 model (i.e. GPR with Harr wavelet) remarkably improves the accuracy of GWL prediction compared to GPR. Finally, the hybrid W-SVR-3 model (i.e. SVR with Symlet) provides the best GWL prediction with RMSE, MAPE, R2, and NS of 0.01290 (m), 0.00079%, 0.99999, and 0.99999, respectively. Overall, the findings indicate that hybrid models can accurately predict GWL in arid regions.
英文关键词	Groundwater level prediction hydrological model Gaussian process regression support vector artificial intelligence machine learning
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000673802000001
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; SUPPORT VECTOR MACHINE ; DATA-DRIVEN TECHNIQUES ; MODEL ; TEMPERATURE ; STREAMFLOW ; SYSTEM ; ANFIS ; COEFFICIENT ; PERFORMANCE
WOS类目	Engineering, Multidisciplinary ; Engineering, Mechanical ; Mechanics
WOS研究方向	Engineering ; Mechanics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/363087
作者单位	[Band, Shahab S.] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu, Yunlin, Taiwan; [Heggy, Essam] Univ Southern Calif, Viterbi Sch Engn, Los Angeles, CA 90007 USA; [Heggy, Essam] CALTECH, Jet Prop Lab, Pasadena, CA USA; [Bateni, Sayed M.] Univ Hawaii Manoa, Dept Civil & Environm Engn, Honolulu, HI 96822 USA; [Bateni, Sayed M.] Univ Hawaii Manoa, Water Resources Res Ctr, Honolulu, HI 96822 USA; [Karami, Hojat; Rabiee, Mobina] Semnan Univ, Civil Engn Dept, Semnan, Iran; [Samadianfard, Saeed] Univ Tabriz, Fac Agr, Dept Water Engn, Tabriz, Iran; [Chau, Kwok-Wing] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China; [Mosavi, Amir] Tech Univ Dresden, Fac Civil Engn, Dresden, Germany; [Mosavi, Amir] Obuda Univ, John von Neumann Fac Informat, Budapest, Hungary; [Mosavi, Amir] J Selye Univ, Dept Informat, Komarno, Slovakia
推荐引用方式 GB/T 7714	Band, Shahab S.,Heggy, Essam,Bateni, Sayed M.,et al. Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression[J],2021,15(1):1147-1158.
APA	Band, Shahab S..,Heggy, Essam.,Bateni, Sayed M..,Karami, Hojat.,Rabiee, Mobina.,...&Mosavi, Amir.(2021).Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression.ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS,15(1),1147-1158.
MLA	Band, Shahab S.,et al."Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression".ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 15.1(2021):1147-1158.