For groundwater conservation and management, it is important to accurately assess groundwater pollution vulnerability. This study proposed an integrated model using ridge regression and a genetic algorithm (GA) to effectively select the major hydro-geological parameters influencing groundwater pollution vulnerability in an aquifer. The GA-Ridge regression method determined that depth to water, net recharge, topography, and the impact of vadose zone media were the hydro-geological parameters that influenced trichloroethene pollution vulnerability in a Korean aquifer. When using these selected hydro-geological parameters, the accuracy was improved for various statistical nonlinear and artificial intelligence (AI) techniques, such as multinomial logistic regression, decision trees, artificial neural networks, and case-based reasoning. These results provide a proof of concept that the GA-Ridge regression is effective at determining influential hydro-geological parameters for the pollution vulnerability of an aquifer, and in turn, improves the AI performance in assessing groundwater pollution vulnerability.
Bibliographical noteFunding Information:
Acknowledgments This research was supported by the Korea Ministry of Environment via the GAIA project (grant number: 141-081-034). In addition, this research was supported by WCU (World Class University) program through the National Research Foundation of Korea funded by the Ministry of Education, Science, and Technology (R33-10076).
All Science Journal Classification (ASJC) codes
- Environmental Science(all)
- Management, Monitoring, Policy and Law