최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기한국물환경학회지 = Journal of Korean Society on Water Environment, v.36 no.4, 2020년, pp.300 - 313
김령은 (부경대학교 지구환경시스템과학부 (환경공학전공)) , 원정은 (부경대학교 지구환경시스템과학부 (환경공학전공)) , 최정현 (부경대학교 지구환경시스템과학부 (환경공학전공)) , 이옥정 (부경대학교 환경공학과) , 김상단 (부경대학교 환경공학과)
The Bayesian approach can be used to estimate hydrologic model parameters from the prior expert knowledge about the parameter values and the observed data. The purpose of this study was to compare the performance of the two Bayesian methods, the Metropolis-Hastings (MH) algorithm and the Generalized...
핵심어 | 질문 | 논문에서 추출한 답변 |
---|---|---|
수문모형의 매개변수를 추정하는데 사용되는 일반적인 두 가지 유형의 정보는 무엇인가? | 정확한 모형 구동과 이에 기초한 올바른 의사 결정을 보장하려면 모형 매개변수의 적절한 추정이 요구된다. 일반적으로 두 가지 유형의 정보(전문가의 지식을 기반으로 한 사전 정보 및 모니터링을 통해 수집된 관측자료)가 수문모형의 매개변수를 추정하는데 사용될 수 있다. 최소자승법을 비롯한 다양한 목적함수를 이용하는 매개변수 자동추정방법에서는 모형의 매개변수 추정을 위하여 관측된 자료만을 사용한다. | |
MH 알고리즘이란? | , 1953). 이 알고리즘은 Markov Chain Monte Carlo(MCMC) 표본 추출 방법의 일종으로, 사후분포로부터 표본을 생성하는 기법이다. MH의 원리는 사후분포의 특성(예를 들어, 매개변수의 평균 또는 표준편차)을 정확하게 결정할 수 있도록 사후분포에서 충분히 큰 표본을 생성하는 것이다. | |
GLUE 방법의 원리는 무엇인가? | , 1999). 이 방법의 원리는 사전분포에서 많은 수의 매개변수 값을 생성하여 매개변수의 공간을 분할하는 것이다. 그런 다음 우도함수를 이용하여 각 매개변수 조합의 가중치를 계산한다. MH 알고리즘과 GLUE 방법은 이미 많은 복잡한 수문모델에 적용되어왔다(Rajib et al. |
Abbaspour, K., Yang, J. Maximov, I., Siber, R., Bogner, K, Mieleitner, J., and Srinivasan, R. (2007). Modelling hydrology and water quality in the pre-Alpine/Alpine thur watershed using SWAT, Journal of Hydrology, 333, 413-430.
Bergstrom, S. (1976). Development and application of a conceptual runoff model for Scandinavian catchments, SMHI Report RHO 7, Norrkoping, 134.
Bernardo, J. and Smith, A. (1994). Bayesian Theory, Wiley Chichester.
Beven, K. (2019). Validation and Equifinality, In: Beisbart C., Saam N. (eds) Computer Simulation Validation. Simulation Foundations, Methods and Applications. Springer, Cham.
Beven, K. and Binley, A. (1992). The future of distributed models: model calibration and uncertainty prediction, Hydrological Process, 6, 279-298.
Campbell, E., Fox, D., and Bates, B. (1999). A Bayesian approach to parameter estimation and pooling in nonlinear flood event models, Water Resources Research, 35, 211-220.
Franks, S., Gineste, P., Beven, K., and Merot, P. (1998). On constraining the predictions of a distributed model: The incorporation of fuzzy estimates of saturated areas into the calibration process, Water Resources Research, 34, 787-797.
Gelman, A. (1995). Inference and monitoring convergence, in: Gilks et al. (Eds.), Markov Chain Monte Carlo in Practice, Chapman & Hall, London, 131-142.
Geyer, C. (1992). Practical Markov chain Monte Carlo, Statistical Science, 7, 473-511.
Gilks, W., Richardson, S., and Spiegelhalter, D. (1995). Introducing Markov Chain Monte Carlo, in: Gilks et al. (Eds.), Markov Chain Monte Carlo in Practice, Chapman & Hall, London, 1-18.
Harmon, R. and Challenor, P. (1997). A Markov chain Monte Carlo method for estimation and assimilation into models, Ecological Modeling, 101, 41-59.
Hastings, W. (1970). Monte Carlo sampling methods using Markov chains and their applications, Biometrika, 57, 97-109.
Kagabu, M., Ide, K., Hosono, T, Nakagawa, K., and Shimada, J. (2020). Describing coseismic groundwater level rise using tank model in volcanic aquifers, Kumamoto, southern Japan, Journal of Hydrology, 582, 124464, https://doi.org/10.1016/j.jhydrol.2019.124464.
Kim, B., Kim, S., Lee, E., and Kim, H. (2007). Methodology for estimating ranges of SWAT model parameters: Application of Imha lake inflow and suspended sediments, Korean Society of Civil Engineers Magazine, 27(B), 661-668. [Korean Literature]
Kim, J. and Kim, S. (2007). Flow duration curve analysis for Nakdong river basin using TMDL flow data, Journal of Korean Society on Water Environment, 23(3), 332-338. [Korean Literature]
Kim, M., Heo, T., and Chung, S. (2013). Uncertainty analysis on the simulations of runoff and sediment using SWAT-CUP, Journal of Korean Society on Water Environment, 29(5), 681-690. [Korean Literature]
Kim, M., Ko, I., and Kim, S. (2009). An analysis of the effect of climate change on Nakdong river flow condition using CGCM's future climate information, Journal of Korean Society on Water Environment, 25(6), 863-871. [Korean Literature]
Kim, S., Kang, D., Kim, M., and Shin, H. (2007). The possibility of daily flow data generation from 8-day intervals measured flow data for calibrating watershed model, Journal of Korean Society on Water Environment, 23(1), 64-71. [Korean Literature]
Kim, S., Lee, K., and Kim, H. (2005). Low flow estimation for river water quality models using a long-term runoff hydrologic model, Journal of Korean Society on Water Environment, 21(6), 575-583. [Korean Literature]
Korea Meteorological Administration (KMA). (2020). Open Weather data portal, https://data.kma.go.kr/cmmn/main.do (accessed May. 2020).
Lee, A. and Kim, S. (2011). An analysis of the effect of climate change on Nakdong river environmental flow, Journal of Korean Society on Water Environment, 27(3), 273-285. [Korean Literature]
Lee, A., Cho, S., Kang, D. K., and Kim, S. (2014). Analysis of the effect of climate change on the Nakdong river stream flow using indicators of hydrological alteration, Journal of Hydro-environmental Research, 8, 234-247.
Lee, A., Cho, S., Park, M. J., and Kim, S. (2013). Determination of standard target water quality in the Nakdong river basin for the total maximum daily load management system in Korea, KSCE Journal of Civil Engineering, 17, 309-319.
Lee, J., Kim, J., Lee, J., Kang, I., and Kim, S. (2012). Current status of refractory dissolved organic carbon in the Nakdong river basin, Journal of Korean Society on Water Environment, 28(4), 538-550. [Korean Literature]
Lee, J., Kim, U., Kim, L. H., Kim, E. S., and Kim, S. (2019). Management of organic matter in watersheds with insufficient observation data: the Nakdong river basin, Desalination and Water Treatment, 152(2019), 44-57, doi: 10.5004/dwt.2019.24021.
Makowski, D., Wallach, D., and Tremblay, M. (2002). Using a Bayesian approach to paramter estimation; Comparison of the GLUE and MCMC methods, Agronomie, 22,191-203.
Malakoff, D. (1999). Bayes offers a 'New' way to make sense of numbers, Science, 286, 1460-1464.
Mckay, M., Baekman, R., and Conover, W. (1979). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 21(2), 239-245.
Metropolis, N., Rosenbluth, A., Rosenbluth, M., and Teller A. H. (1953). Equation of state calculations by fast computing machines, Journal of Chemical Physics, 21, 1087-1091.
Ministry of Environment (ME). (2020). Water Environment Information System (WEIS), http://water.nier.go.kr/publicMain/mainContent.do (accessed May. 2020).
Parajka, J., Merz, R., and Bloschl, G. (2005). A comparison of regionalisation methods for catchment model parameters, Hydrology and Earth System Sciences, 9, 157-171.
Perrin, C., Michel, C., and Andreassian, V. (2003). Improvement of a parsimonious model for streamflow simulation, Journal of Hydrology, 279, 275-289.
Pushpalatha, R., Perrin, C., Le Moine, N., Mathevet, T., and Andreassian, V. (2011). A downward structural sensitivity analysis of hydrological models to improve low-flow simulation, Journal of Hydrology, 411(1-2), 66-76.
Raftery A. and Lewis S. (1995). Implementing MCMC, in: Gilks et al. (Eds.), Markov Chain Monte Carlo in Practice, Chapman & Hall, London, 115-130.
Rajib, M., Merwade, V., and Yu, Z. (2016). Multi-objective calibration of a hydrologic model using spatially distributed remotely sensed/in-situ soil moisture, Journal of Hydrology, 536, 192-207.
Ryu, J., Kang, H., Choi, J., Kong, D., Gum, D., Jang, C., and Lim, K. (2012). Application of SWAT-CUP for streamflow auto-calibration at Soyang-gang dam watershed, Journal of Korean Society on Water Environment, 28(3), 347-358. [Korean Literature]
Shulz K., Beven, K., and Huwe B. (1999). Equifinality and the problem of robust calibration in nitrogen budget simulations, Soil Science Society of America Journal, 63, 1934-1941.
Sugawara, M. (1979). Automatic calibration of the tank model, Hydrological Sciences Bulletin, 24(3), 375-388.
Sun, M., Zhang, X., Huo, Z., Feng, S., Huang, G., and Mao, X. (2016). Uncertainty and sensitivity assessment of an agricultural-hydrological model, Journal of Hydrology, 534, 19-30.
Wallach, D. (1995). Regional optimization of fertilization using a hierarchical linear model, Biometrics, 51, 338-346.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.