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수문모형 검보정을 위한 새로운 최적화 목적함수의 소개 및 이론적 특성 고찰  

Choe, Hyeon-Il (영남대학교 건설시스템공학과)
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Water for future / v.55, no.6, 2022 , pp. 52-60 More about this Journal
Keywords
Citations & Related Records
연도 인용수 순위
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1 Bastidas, L., Gupta, H.V., Sorooshian, S., Shuttleworth, W.J., Yang, Z., 1999. Sensitivity analysis of a land surface scheme using multicriteria methods. J. Geophys. Res. Atmos. 104(D16), 19481-19490.   DOI
2 Bastidas, L.A., Hogue, T.S., Sorooshian, S., Gupta, H.V., Shuttleworth, W.J., 2006. Parameter sensitivity analysis for different complexity land surface models using multicriteria methods. J. Geophys. Res. Atmos. 111(D20). DOI:https://doi.org/10.1029/2005JD006377.   DOI
3 Beven, K., 2001. How far can we go in distributed hydrological modelling? Hydrol. Earth Syst. Sci. 5(1), 1-12.   DOI
4 Choi, H.I., 2022. Comment on Liu (2020): A rational performance criterion for hydrological model. J. Hydrol. 606, 126927.   DOI
5 Gupta, H.V., Sorooshian, S., Yapo, P.O., 1998. Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information. Water Resour. Res. 34(4), 751-763.   DOI
6 Lee, J.S., Choi, H.I., 2022. A rebalanced performance criterion for hydrological model calibration. J. Hydrol. 606, 127372.   DOI
7 Liu, D., 2020. A rational performance criterion for hydrological model. J. Hydrol. 590, 125488. DOI:https://doi.org/10.1016/j.jhydrol.2020.125488.   DOI
8 Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., Veith, T.L., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50(3), 885-900. DOI:https://doi.org/10.13031/2013.23153.   DOI
9 Murphy, A.H., 1988. Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon. Weather Rev. 116(12), 2417-2424.   DOI
10 Neelin, J.D., Bracco, A., Luo, H., McWilliams, J.C., Meyerson, J.E., 2010. Considerations for parameter optimization and sensitivity in climate models. Proc. Natl. Acad. Sci. 107(50), 21349-21354. DOI:10.1073/pnas.1015473107.   DOI
11 Gupta, H.V., Kling, H., Yilmaz, K.K., Martinez, G.F., 2009. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J. Hydrol. 377, 80-91.   DOI
12 Pineiro, G., Perelman, S., Guerschman, J.P., Paruelo, J.M., 2008. How to evaluate models: Observed vs. predicted or predicted vs. observed? Ecol. Modell. 216(3), 316-322. DOI:https://doi.org/10.1016/j.ecolmodel.2008.05.006.   DOI
13 Weglarczyk, S., 1998. The interdependence and applicability of some statistical quality measures for hydrological models. J. Hydrol. 206(1), 98-103. DOI:https://doi.org/10.1016/S0022-1694(98)00094-8.   DOI
14 Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I - A discussion of principles. J. Hydrol. 10(3): 282-290. DOI:https://doi.org/10.1016/0022-1694(70)90255-6.   DOI
15 van Werkhoven, K., Wagener, T., Reed, P., Tang, Y., 2009. Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models. Adv. Water Resour. 32(8), 1154-1169. DOI:https://doi.org/10.1016/j.advwatres.2009.03.002.   DOI