Groundwater Level Trend Analysis for Long-term Prediction Basedon Gaussian Process Regression
Kim, Hyo Geon;Park, Eungyu;Jeong, Jina;Han, Weon Shik;Kim, Kue-Young;
Department of Geology, Kyungpook National University;Department of Geology, Kyungpook National University;Department of Geology, Kyungpook National University;Department of Earth System Sciences, Yonsei University;Korea Institute of Geoscience and Mineral Resources;
1. Bazi, Y., Alajlan, N., and Melgani, F., 2012, Improved estimation of water chlorophyll concentration with semisupervised gaussian process regression, IEEE Transactions on Geoscience and Remote Sensing, 50(7), 2733-2743.
2. Cha, K., Cheong, T.S., and Ko, I., 2007, Validation of the surface-ground waters interaction and water supplying to upper region of geum river basin by optimal method for drought season, J. Korean Soc. Civil Eng. B, 27(5B), 507-513.
3. Evans, James D., 1996, Straightforward statistics for the behavioral sciences, Brooks/Cole, 600 p.
4. GIMS (National Groundwater Information Management and Service Center), 2015, available at http://www.gims.go.kr.
5. Grbić, R., Kurtagić , D., and Slišković, D., 2013, Stream water temperature prediction based on Gaussian process regression Expert Systems with Applications, 40(18), 7407-7414.
6. Jang, S., Hamm, S.Y., Yoon, H., Kim, G.B., Park, J.H., and Kim, M.S., 2015, Predicting long-term change of groundwater level with regional climate model in South Korea, Geosci. J., 19(3), 503-513.
7. Jeong, J.M., Park, Y.C., Jo, Y.J., and Lee, J.Y., 2010, Time series analysis of groundwater level fluctuation data in Cheonjeonri, Chuncheon, Gangwon-do, J. Geol. Soc. Korea, 46(2), 171-176.
8. Kim, B.S., Kwon, H.H., and Kim, H.S., 2011, Impact assessment of climate change on drought risk, J. Wetlands Res., 13(1), 1-11.
9. Kim, C.R., Kim, Y.O., Seo, S.B., and Choi, S.W., 2013, Water balance projection using climate change scenarios in the Korean Peninsula, J. Korea Water Resources Association, 46(8), 807-819.
10. Kim, G., Choi, D., and Shin, S., 2011, Characteristics of groundwater levels fluctuation and quality in Ddan-sum area, J. Korean Geoenviron. Soc., 12(2), 35-43.
11. Kim, G.B. and Lee, S.H., 2012, Applicability of logistic regression model of groundwater levels to drought forecast, J. Geological Soc., 48(3), 275-284.
12. Kim, G.B. and Yum, B.W., 2007, Classification and characterization for water level time series of shallow wells at the national groundwater monitoring stations, J. Soil Groundw. Environ., 12(5), 86-97.
13. Kim, G.B., Yun, H.H., and Kim, D.H., 2006, Relationship between standardized precipitation index and groundwater levels: A proposal for establishment of drought index wells, J. Soil Groundw. Environ., 11(3), 31-42.
14. Kim, T.W. and Park, D.H., 2015, Extreme drought response and improvement - Focusing on 2015 drought, J. Korean Soc. Civil Eng., 63(9), 25-35.
15. Kim, Y.K., Yoo, J.A., and Chung, E.S., 2012, Water management vulnerability assessment considering climate change in Korea, J. Clim. Change Res., 3(1), 1-12.
16. KMA (Korea Meteorological Administration), 2015, available at http://www.kma.go.kr.
17. Kwon, H.J. and Kim, S.J., 2007, Methodology of drought assessment using national groundwater monitoring network data, J. Korean Soc. Civil Eng. B, 27(2B), 193-199.
18. Lee, B., Hamm, S.Y., Jang, S., Cheong, J.Y., and Kim, G.B., 2014, Relationship between groundwater and climate change in South Korea, Geosci. J., 18(2), 209-218.
19. Lee, B.S., Kim, Y.I., Choi, K.J., Song, S.H., Kim, J.H., Woo, D.K., Seol, M.K., and Park, K.Y., 2014, Rural groundwater monitoring network in Korea, J. Soil Ground. Environ., 19(4), 1-11.
20. Lee, J.Y., Yi, M.J., Lee, J.M., Ahn, K.H., Won, J.H., Moon, S.H., and Cho, M., 2006, Parametric and non-parametric trend analysis of groundwater data obtained from national groundwater monitoring stations, J. Soil Groundw. Environ., 11(2), 56-67.
21. MOLIT (Ministry of Land, Infrastructure and Transport), 2011, Analysis report for long-term measured data from the national groundwater monitoring network in the Yeongsan and Seomjin River Basins, 211 p.
22. MOLIT (Ministry of Land, Infrastructure and Transport), 2014, Annual report for national groundwater monitoring network in Korea, 738 p.
23. Park, E., 2012, Delineation of recharge rate from a hybrid water table fluctuation method, Water Resources Research, 48(7), W07503.
24. Park, E. and Parker, J.C., 2008, A simple model for water table fluctuations in response to precipitation, J. Hydrol., 365(3-4), 344-349.
25. Park, J.Y., Yoo, J.Y., Lee, M., and Kim, T.W., 2012, Assessment of drought risk in Korea: Focused on data-based drought risk map, J. Korean Soc. Civil Eng. B, 32(4B), 203-211.
26. Rasmussen, C.E., 2004, Gaussian processes in machine learning. In: Bousquet, O., von Luxburg, U. and Ratsch, G. (eds.), Adv. Lect. Mach. Learn., Springer, Berlin, p. 63-71.
27. Rasmussen, C.E. and Williams, C.K.I., 2006, Gaussian Processes for Machine Learning, The MIT Press, Cambridge, 266 p.
28. Sun, A.Y., Wang, D., and Xu, X., 2014, Monthly streamflow forecasting using Gaussian Process Regression, J. Hydrol., 511, 72-81.
29. Yang, J.S., Park, J.H., and Kim, N.K., 2012, Development of drought vulnerability index using trend analysis, J. Korean Soc. Civil Eng. B, 32(3), 185-192.
30. Yi, M.J., Kim, G.B., Sohn, Y.C., Lee, J.Y., and Lee, K.K., 2004, Time series analysis of groundwater level data obtained from national groundwater monitoring stations, J. Geol. Soc. Korea, 40(3), 305-329.