A subagging regression method for estimating the qualitative and quantitative state of groundwater

Jina Jeong, Eungyu Park, Weon Shik Han, Kue Young Kim

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


A subsample aggregating (subagging) regression (SBR) method for the analysis of groundwater data pertaining to trend-estimation-associated uncertainty is proposed. The SBR method is validated against synthetic data competitively with other conventional robust and non-robust methods. From the results, it is verified that the estimation accuracies of the SBR method are consistent and superior to those of other methods, and the uncertainties are reasonably estimated; the others have no uncertainty analysis option. To validate further, actual groundwater data are employed and analyzed comparatively with Gaussian process regression (GPR). For all cases, the trend and the associated uncertainties are reasonably estimated by both SBR and GPR regardless of Gaussian or non-Gaussian skewed data. However, it is expected that GPR has a limitation in applications to severely corrupted data by outliers owing to its non-robustness. From the implementations, it is determined that the SBR method has the potential to be further developed as an effective tool of anomaly detection or outlier identification in groundwater state data such as the groundwater level and contaminant concentration.

Original languageEnglish
Pages (from-to)1491-1500
Number of pages10
JournalHydrogeology Journal
Issue number5
Publication statusPublished - 2017 Aug 1

Bibliographical note

Funding Information:
The financial support of this study was provided by Korea Environmental Industry and Technology Institute (KEITI) (project title: Environmental Management of Geologic CO Storage, project 2014001810004). 2

Publisher Copyright:
© 2017, Springer-Verlag Berlin Heidelberg.

All Science Journal Classification (ASJC) codes

  • Water Science and Technology
  • Earth and Planetary Sciences (miscellaneous)


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