- Applications of Gaussian Process Regression to Groundwater Quality Data
- Koo, Min-Ho;Park, Eungyu;Jeong, Jina;Lee, Heonmin;Kim, Hyo Geon;Kwon, Mijin;Kim, Yongsung;Nam, Sungwoo;Ko, Jun Young;Choi, Jung Hoon;Kim, Deog-Geun;Jo, Si-Beom;
- Department of Geoenvironmental Sciences, Kongju National University;Department of Geology, Kyungpook National University;Department of Geology, Kyungpook National University;Department of Geology, Kyungpook National University;Byucksan Engineering;Korea Radioactive Waste Agency;GeoGreen21 Co. Ltd.;GeoGreen21 Co. Ltd.;Dohwa Engineering;GeoInnovation;Korea Water Resources Corporation;Korea Rural Community Corporation;
- 가우시안 프로세스 회귀분석을 이용한 지하수 수질자료의 해석
- 구민호;박은규;정진아;이헌민;김효건;권미진;김용성;남성우;고준영;최정훈;김덕근;조시범;
- 공주대학교 지질환경과학과;경북대학교 지질학과;경북대학교 지질학과;경북대학교 지질학과;벽산엔지니어링;한국원자력환경공단;지오그린21;지오그린21;도화엔지니어링;(주) 지오이노베이션;한국수자원공사;한국농어촌공사;
Abstract
Gaussian process regression (GPR) is proposed as a tool of long-term groundwater quality predictions. The major advantage of GPR is that both prediction and the prediction related uncertainty are provided simultaneously. To demonstrate the applicability of the proposed tool, GPR and a conventional non-parametric trend analysis tool are comparatively applied to synthetic examples. From the application, it has been found that GPR shows better performance compared to the conventional method, especially when the groundwater quality data shows typical non-linear trend. The GPR model is further employed to the long-term groundwater quality predictions based on the data from two domestically operated groundwater monitoring stations. From the applications, it has been shown that the model can make reasonable predictions for the majority of the linear trend cases with a few exceptions of severely non-Gaussian data. Furthermore, for the data shows non-linear trend, GPR with mean of second order equation is successfully applied.
Keywords: Groundwater quality;Trend analysis;Gaussian process regression;Theil-Sen estimator;Groundwater quality monitoring network;
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