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Cessation time approach incorporating parametric and non-parametric machine-learning algorithms for recovery test data.
- Source :
- Hydrological Sciences Journal/Journal des Sciences Hydrologiques; 2023, Vol. 68 Issue 11, p1578-1590, 13p
- Publication Year :
- 2023
-
Abstract
- In this study we propose a new method called the cessation time approach (CTA) for interpreting recovery tests in confined aquifers, which is based on the Theis solution. The CTA method involves selecting a residual drawdown measurement from the recovery phase and linking it to its dimensionless counterpart through simple algebraic steps. This approach is then incorporated with a regression model to estimate aquifer parameters. The performance of several parametric polynomial and non-parametric machine learning regression models was investigated using various datasets. Results show that CTA with third-order multivariable polynomials produced highly accurate parameter estimates with a normalized root mean squared error (NRMSE) within 0.5% for a field dataset. Among the machine learning algorithms tested, the radial basis function and Gaussian process regression achieved the highest accuracy with NRMSEs of 0.6%. We conclude that CTA can be a viable interpretation tool for recovery tests due to its accuracy and simplicity. [ABSTRACT FROM AUTHOR]
- Subjects :
- DATA recovery
MACHINE learning
STANDARD deviations
KRIGING
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 02626667
- Volume :
- 68
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- Hydrological Sciences Journal/Journal des Sciences Hydrologiques
- Publication Type :
- Academic Journal
- Accession number :
- 170718263
- Full Text :
- https://doi.org/10.1080/02626667.2023.2230202