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.
Journal of Cleaner Production – Elsevier
Published: May 20, 2016
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera