Journal of Soil and Groundwater Environment (한국지하수토양환경학회지:지하수토양환경)

Korean Society of Soil and Groundwater Environment (한국지하수토양환경학회)
권호 표지
DOI QR코드

DOI QR Code

Applications of Gaussian Process Regression to Groundwater Quality Data

가우시안 프로세스 회귀분석을 이용한 지하수 수질자료의 해석

  • Received : 2016.09.24
  • Accepted : 2016.12.21
  • Published : 2016.12.31
Download PDF
⟨ Previous Next ⟩

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

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. https://doi.org/10.1109/TGRS.2011.2174246
  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. https://doi.org/10.1016/j.eswa.2013.06.077
  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. https://doi.org/10.1111/j.1752-1688.1988.tb00896.x
  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. https://doi.org/10.1029/WR018i001p00107
  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. https://doi.org/10.1029/91WR00259
  10. Jarque, Carlos M., Bera, Anil K., 1987, A test for normality of observations and regression residuals, Int. Stat. Rev., 55(2), 163-172. https://doi.org/10.2307/1403192
  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. https://doi.org/10.1016/j.jhydrol.2014.01.023