• 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;
References
  • 1. Kim, G.B., Choi, D.H., Yoon, P.S., and Kim, K.Y., 2010, Trends of groundwater quality in the areas with a high possibility of pollution, J. Korean Geo-Environ. Soc., 11(3), 5-16.
  •  
  • 2. Ministry of Environment (Korea), National Institute of Environmental Research (Korea), 2007-2013, National Groundwater Quality Monitoring Network Annual Report.
  •  
  • 3. Bazi, Y., Alajlan, N., and Melgani, F., 2012, Improved Estimation of Water Chlorophyll Concentration With Semisupervised Gaussian Process Regression, IEEE Trans. Geosci. Remote Sensing, 50(7), 2733-2743.
  •  
  • 4. Chapman, D., 1996, Water quality assessments: a guide to the use of biota, sediments, and water in environmental monitoring, UNESCO/WHO/UNEP, 22 p.
  •  
  • 5. Grbi, R., Kurtagi, D., and Sli kovi, D., 2013, Stream water temperature prediction based on Gaussian process regression, Expert Sys. Applic., 40(18), 7407-7414.
  •  
  • 6. Helsel, D.R. and Hirsch, R.M., 1988, Applicability of the t-Test for Detecting Trends in Water Quality Variables, J. American Water Resour. Assoc., 24(1), 201-204.
  •  
  • 7. Helsel, D.R. and Hirsch, R.M., 2002, Statistical methods in water resources: US Geological Survey Techniques of Water Resources Investigations, book 4, chap. A3, U.S. Geological Survey.
  •  
  • 8. Hirsch, R.M., Slack, J.R., and Smith, R.A., 1982, Techniques of trend analysis for monthly water-quality data, Water Resour. Res., 18, 107-121.
  •  
  • 9. Hirsch, R.M., Alexander, R.B., and Smith, R.A., 1991, Selection of methods for the detection and estimation of trends in water quality, Water Resour. Res., 27(5), 803-813.
  •  
  • 10. Jarque, Carlos M., Bera, Anil K., 1987, A test for normality of observations and regression residuals, Int. Stat. Rev., 55(2), 163-172.
  •  
  • 11. Murphy, K.P., 2012, Machine Learning: a Probabilistic Perspective, The MIT Press, Cambridge, 1067 p.
  •  
  • 12. Sun, A.Y., Wang, D., and Xu, X., 2014, Monthly streamflow forecasting using Gaussian Process Regression, J. Hydro., 511, 72-81.
  •  

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