Cessation time approach incorporating parametric and non-parametric machine-learning algorithms for recovery test data

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dc.contributor.authorŞahin, A. Ufuk
dc.contributor.authorÇiftçi, Emin
dc.date.accessioned2024-07-12T21:40:19Z
dc.date.available2024-07-12T21:40:19Z
dc.date.issued2023en_US
dc.department[Belirlenecek]en_US
dc.description.abstractIn 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.en_US
dc.identifier.doi10.1080/02626667.2023.2230202
dc.identifier.endpage1590en_US
dc.identifier.issn0262-6667
dc.identifier.issn2150-3435
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85165197076en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1578en_US
dc.identifier.urihttps://doi.org/10.1080/02626667.2023.2230202
dc.identifier.urihttps://hdl.handle.net/20.500.12415/7246
dc.identifier.volume68en_US
dc.identifier.wosWOS:001027585300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofHydrological Sciences Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKY05202
dc.subjectAquifer Analysisen_US
dc.subjectMachine Learningen_US
dc.subjectNon-Parametric Algorithmsen_US
dc.subjectParameter Estimationen_US
dc.subjectRecovery Testen_US
dc.subject>en_US
dc.titleCessation time approach incorporating parametric and non-parametric machine-learning algorithms for recovery test dataen_US
dc.typeArticle
dspace.entity.typePublication

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