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

Shahab S. Band, Essam Heggy, Sayed M. Bateni, Hojat Karami, Mobina Rabiee, Saeed Samadianfard, Kwok Wing Chau, Amir Mosavi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1147-1158
Number of pages12
JournalEngineering Applications of Computational Fluid Mechanics
Volume15
Issue number1
DOIs
Publication statusPublished - 15 Jul 2021

Keywords

  • artificial intelligence
  • Gaussian process regression
  • Groundwater level prediction
  • hydrological model
  • machine learning
  • support vector

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation

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