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

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
Main Authors: Shahab S. Band, Essam Heggy, Sayed M. Bateni, Hojat Karami, Mobina Rabiee, Saeed Samadianfard, Kwok-Wing Chau, Amir Mosavi
Format: Article
Language:English
Published: Taylor & Francis Group 2021-01-01
Series:Engineering Applications of Computational Fluid Mechanics
Subjects:
groundwater level prediction
hydrological model
gaussian process regression
support vector
artificial intelligence
machine learning
Online Access:http://dx.doi.org/10.1080/19942060.2021.1944913

Internet

http://dx.doi.org/10.1080/19942060.2021.1944913

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