Decision tree-based data mining and rule induction for identifying hydrogeological parameters that influence groundwater pollution sensitivity

Keunje Yoo, Sudheer Kumar Shukla, Jae Joon Ahn, Kyungjoo Oh, Joonhong Park

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)


This study aims to develop a new field-based approach that can estimate patterns of groundwater pollution sensitivity using data mining algorithms. Hydrogeological and pollution sensitivity data were collected from the Woosan Industrial Complex, Korea, which is a site contaminated by trichloroethylene (TCE). The proposed data mining algorithm procedure uses seven hydrogeological properties as input variables: depth to water, net recharge, aquifer media, soil media, topography, vadose zone media, and hydraulic conductivity. The observed TCE sensitivity was used as the target data. Initially, four data mining algorithms artificial neural network (ANN), decision tree (DT), case-based reasoning (CBR), and multinomial logistic regression (MLR) were tested. We found that the DT-based data mining and rule induction method shows better prediction accuracy and consistency than the other methods. We also used the ordinal pairwise partitioning (OPP) algorithm to improve the accuracy and consistency of the DT model. A classification and regression tree (CART) analysis of the OPP-DT model indicated that the net recharge (R), soil media (S), and aquifer media (A) were the major hydrogeological factors that influence groundwater sensitivity to TCE at the site. The results of this study demonstrate that the proposed model can provide more accurate and consistent estimates of groundwater vulnerability to TCE compared to the existing models.

Original languageEnglish
Pages (from-to)277-286
Number of pages10
JournalJournal of Cleaner Production
Publication statusPublished - 2016 May 20

Bibliographical note

Funding Information:
This work was supported by the Korea Ministry of Environment via a grant from The GAIA Project. In addition, this work was partially supported by the National Research Foundation (NRF) of Korea via a grant (No. 2011-0030040) funded by the Korea government (MSIP).

Publisher Copyright:
© 2016 Elsevier Ltd. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • Environmental Science(all)
  • Strategy and Management
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Decision tree-based data mining and rule induction for identifying hydrogeological parameters that influence groundwater pollution sensitivity'. Together they form a unique fingerprint.

Cite this