Using GA-Ridge regression to select hydro-geological parameters influencing groundwater pollution vulnerability |
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Authors: | Jae Joon Ahn Young Min Kim Keunje Yoo Joonhong Park Kyong Joo Oh |
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Institution: | School of Information and Industrial Engineering, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, 120-749, South Korea. |
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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. |
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