title: Using GA-Ridge regression to select hydro-geological parameters influencing groundwater pollution vulnerability.
abstract: 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.
authors: School of Information and Industrial Engineering, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul 120-749, South Korea.
authors: Jae Joon
authors: JJ
authors: Ahn
authors: Young Min
authors: YM
authors: Kim
authors: Keunje
authors: K
authors: Yoo
authors: Joonhong
authors: J
authors: Park
authors: Kyong Joo
authors: KJ
authors: Oh
mesh_terms: D000465:Algorithms; D004784:Environmental Monitoring / Q000379:methods; D060587:Groundwater / Q000737:chemistry* / Q000592:standards; D008956:Models, Chemical; D056910:Republic of Korea; D014241:Trichloroethylene / Q000032:analysis; D014874:Water Pollutants, Chemical / Q000032:analysis; D014876:Water Pollution / Q000706:statistics & numerical data*
chemical_list: D014874:Water Pollutants, Chemical; D014241:Trichloroethylene