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

Decision tree-based data mining and rule induction for identifying hydrogeological parameters... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Cleaner Production Elsevier

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

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Publisher
Elsevier
Copyright
Copyright © 2016 Elsevier Ltd
ISSN
0959-6526
D.O.I.
10.1016/j.jclepro.2016.01.075
Publisher site
See Article on Publisher Site

Abstract

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

Journal of Cleaner ProductionElsevier

Published: May 20, 2016

References

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