Şahin, A. UfukÇiftçi, Emin2024-07-122024-07-1220230262-66672150-343510.1080/02626667.2023.22302022-s2.0-85165197076https://doi.org/10.1080/02626667.2023.2230202https://hdl.handle.net/20.500.12415/7246In 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.eninfo:eu-repo/semantics/closedAccessAquifer AnalysisMachine LearningNon-Parametric AlgorithmsParameter EstimationRecovery Test>Article159011Q1157868WOS:001027585300001N/A