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

ŞAHİN A. U., Çiftçi E.

Hydrological Sciences Journal, vol.68, no.11, pp.1578-1590, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 68 Issue: 11
  • Publication Date: 2023
  • Doi Number: 10.1080/02626667.2023.2230202
  • Journal Name: Hydrological Sciences Journal
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, IBZ Online, PASCAL, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Compendex, Geobase, INSPEC, Pollution Abstracts, Civil Engineering Abstracts
  • Page Numbers: pp.1578-1590
  • Keywords: aquifer analysis, machine learning, non-parametric algorithms, parameter estimation, recovery test
  • Hacettepe University Affiliated: Yes


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.