Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression

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 wav...

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Bibliographic Details
Published in:Engineering applications of computational fluid mechanics 2021-01, Vol.15 (1), p.1147-1158
Main Authors: Band, Shahab S., Heggy, Essam, Bateni, Sayed M., Karami, Hojat, Rabiee, Mobina, Samadianfard, Saeed, Chau, Kwok-Wing, Mosavi, Amir
Format: Article
Language:English
Subjects:
Arid regions
artificial intelligence
Gaussian process
Gaussian process regression
Groundwater
Groundwater level prediction
Groundwater levels
hydrological model
machine learning
Regression
Root-mean-square errors
Statistical analysis
support vector
Support vector machines
Wavelet analysis
Wavelet transforms
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Summary: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.
ISSN:1994-2060
1997-003X
DOI:10.1080/19942060.2021.1944913