메뉴 건너뛰기




Volumn 16, Issue 1, 2014, Pages 95-113

Encapsulation of parametric uncertainty statistics by various predictive machine learning models: MLUE method

Author keywords

Hydrological modelling; Machine learning; MLUE; Monte Carlo; Uncertainty analysis

Indexed keywords


EID: 84897488959     PISSN: 14647141     EISSN: None     Source Type: Journal    
DOI: 10.2166/hydro.2013.242     Document Type: Article
Times cited : (10)

References (55)
  • 1
    • 0034254196 scopus 로고    scopus 로고
    • Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments
    • Abrahart, R. J. & See, L. 2000 Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrological Processes 14, 2157-2172.
    • (2000) Hydrological Processes , vol.14 , pp. 2157-2172
    • Abrahart, R.J.1    See, L.2
  • 2
  • 4
    • 0003235818 scopus 로고
    • Development and application of a conceptual runoff model for Scandinavian catchments
    • Norrk̈ping, Sweden
    • Bergstr̈m, S. 1976 Development and application of a conceptual runoff model for Scandinavian catchments. SMHI Reports RHO, No. 7, Norrk̈ping, Sweden.
    • (1976) SMHI Reports RHO, No. 7
    • Bergstr̈m, S.1
  • 5
    • 0027009437 scopus 로고
    • The future of distributed models: Model calibration and uncertainty prediction
    • Beven, K. & Binley, A. 1992 The future of distributed models: Model calibration and uncertainty prediction. Hydrological Processes 6, 279-298.
    • (1992) Hydrological Processes , vol.6 , pp. 279-298
    • Beven, K.1    Binley, A.2
  • 6
    • 0035426008 scopus 로고    scopus 로고
    • Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology
    • Beven, K. & Freer, J. 2001 Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. Journal of Hydrology 249,11-29.
    • (2001) Journal of Hydrology , vol.249 , pp. 11-29
    • Beven, K.1    Freer, J.2
  • 7
    • 40849146963 scopus 로고    scopus 로고
    • Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling
    • Blasone, R., Vrugt, J., Madsen, H., Rosbjerg, D., Robinson, B. & Zyvoloski, G. 2008 Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling. Advances in Water Resources 31, 630-648.
    • (2008) Advances in Water Resources , vol.31 , pp. 630-648
    • Blasone, R.1    Vrugt, J.2    Madsen, H.3    Rosbjerg, D.4    Robinson, B.5    Zyvoloski, G.6
  • 8
    • 0034749335 scopus 로고    scopus 로고
    • Hydrological modelling using artificial neural networks
    • Dawson, C. W. & Wilby, R. L. 2001 Hydrological modelling using artificial neural networks. Progress in Physical Geography 25, 80-108.
    • (2001) Progress in Physical Geography , vol.25 , pp. 80-108
    • Dawson, C.W.1    Wilby, R.L.2
  • 10
    • 0026445234 scopus 로고
    • Effective and efficient global optimization for conceptual rainfall-runoff models
    • Duan, Q., Sorooshian, S. & Gupta, V. 1992 Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research 28, 1015-1031.
    • (1992) Water Resources Research , vol.28 , pp. 1015-1031
    • Duan, Q.1    Sorooshian, S.2    Gupta, V.3
  • 11
    • 77958183722 scopus 로고    scopus 로고
    • Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology
    • Elshorbagy, A., Corzo, G., Srinivasulu, S. & Solomatine, D. P. 2010a Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: concepts and methodology. Hydrology and Earth System Sciences 14, 1931-1941.
    • (2010) Hydrology and Earth System Sciences , vol.14 , pp. 1931-1941
    • Elshorbagy, A.1    Corzo, G.2    Srinivasulu, S.3    Solomatine, D.P.4
  • 12
    • 77958199170 scopus 로고    scopus 로고
    • Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application
    • Elshorbagy, A., Corzo, G., Srinivasulu, S. & Solomatine, D. P. 2010b Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: application. Hydrology and Earth System Sciences 14,1943-1961.
    • (2010) Hydrology and Earth System Sciences , vol.14 , pp. 1943-1961
    • Elshorbagy, A.1    Corzo, G.2    Srinivasulu, S.3    Solomatine, D.P.4
  • 13
    • 0029659801 scopus 로고    scopus 로고
    • Bayesian estimation of uncertainty in runoff prediction and the value of data: An application of the GLUE approach
    • Freer, J., Beven, K. & Ambroise, B. 1996 Bayesian estimation of uncertainty in runoff prediction and the value of data: an application of the GLUE approach. Water Resources Research 32, 2161-2173.
    • (1996) Water Resources Research , vol.32 , pp. 2161-2173
    • Freer, J.1    Beven, K.2    Ambroise, B.3
  • 14
    • 4143139874 scopus 로고    scopus 로고
    • Towards the characterization of streamflow simulation uncertainty through multimodel ensembles
    • Georgakakos, K., Seo, D.-J., Gupta, H. V., Schaake, J. & Butts, M. M. 2004 Towards the characterization of streamflow simulation uncertainty through multimodel ensembles. Journal of Hydrology 298, 222-241
    • (2004) Journal of Hydrology , vol.298 , pp. 222-241
    • Georgakakos, K.1    Seo, D.-J.2    Gupta, H.V.3    Schaake, J.4    Butts, M.M.5
  • 17
    • 2542498802 scopus 로고    scopus 로고
    • Handling uncertainty in extreme or unrepeatable hydrological processes - The need for an alternative paradigm
    • Hall, J. & Anderson, M. G. 2002 Handling uncertainty in extreme or unrepeatable hydrological processes - the need for an alternative paradigm. Hydrological Processes 16, 1867-1870
    • (2002) Hydrological Processes , vol.16 , pp. 1867-1870
    • Hall, J.1    Anderson, M.G.2
  • 18
    • 0024656259 scopus 로고
    • Probabilistic estimates for multivariate analyses
    • Harr, M. 1989 Probabilistic estimates for multivariate analyses. Applied Mathematical Modeling 13, 313-318.
    • (1989) Applied Mathematical Modeling , vol.13 , pp. 313-318
    • Harr, M.1
  • 19
    • 0016964114 scopus 로고
    • Parameter optimization for watershed models
    • Johnston, P. & Pilgrim, D. 1976 Parameter optimization for watershed models. Water Resources Research 12, 477-486.
    • (1976) Water Resources Research , vol.12 , pp. 477-486
    • Johnston, P.1    Pilgrim, D.2
  • 20
    • 1642348122 scopus 로고    scopus 로고
    • Reduction of Monte-Carlo simulation runs for uncertainty estimation in hydrological modelling
    • Khu, S.-T. & Werner, M. G. F. 2003 Reduction of Monte-Carlo simulation runs for uncertainty estimation in hydrological modelling. Hydrology and Earth System Sciences 7, 680-692.
    • (2003) Hydrology and Earth System Sciences , vol.7 , pp. 680-692
    • Khu, S.-T.1    Werner, M.G.F.2
  • 21
    • 0032214428 scopus 로고    scopus 로고
    • Monte Carlo assessment of parameter uncertainty in conceptual catchment models: The Metropolis algorithm
    • Kuczera, G. & Parent, E. 1998 Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm. Journal of Hydrology 211,69-85.
    • (1998) Journal of Hydrology , vol.211 , pp. 69-85
    • Kuczera, G.1    Parent, E.2
  • 22
    • 41649121626 scopus 로고    scopus 로고
    • Fast and efficient optimization of hydrologic model parameters using a priori estimates and stepwise line search
    • Kuzmin, V., Seo, D.-J. & Koren, V. 2008 Fast and efficient optimization of hydrologic model parameters using a priori estimates and stepwise line search. Journal of Hydrology 353, 109-128.
    • (2008) Journal of Hydrology , vol.353 , pp. 109-128
    • Kuzmin, V.1    Seo, D.-J.2    Koren, V.3
  • 23
    • 0034739246 scopus 로고    scopus 로고
    • Automatic calibration of a conceptual rainfallrunoff model using multiple objectives
    • Madsen, H. 2000 Automatic calibration of a conceptual rainfallrunoff model using multiple objectives. Journal of Hydrology 235, 276-288.
    • (2000) Journal of Hydrology , vol.235 , pp. 276-288
    • Madsen, H.1
  • 24
    • 0033957764 scopus 로고    scopus 로고
    • Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications
    • Maier, H. R. & Dandy, G. C. 2000 Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling & Software 15, 101-124.
    • (2000) Environmental Modelling & Software , vol.15 , pp. 101-124
    • Maier, H.R.1    Dandy, G.C.2
  • 25
    • 77951175284 scopus 로고    scopus 로고
    • Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
    • Maier, H. R., Jain, A., Dandy, G. C. & Sudheer, K. P. 2010 Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environmental Modelling & Software 25, 891-909.
    • (2010) Environmental Modelling & Software , vol.25 , pp. 891-909
    • Maier, H.R.1    Jain, A.2    Dandy, G.C.3    Sudheer, K.P.4
  • 26
    • 33748808177 scopus 로고    scopus 로고
    • Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology
    • Mantovan, P. & Todini, E. 2006 Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology. Journal of Hydrology 330, 368-381.
    • (2006) Journal of Hydrology , vol.330 , pp. 368-381
    • Mantovan, P.1    Todini, E.2
  • 27
    • 7244247371 scopus 로고    scopus 로고
    • Treatment of precipitation uncertainty in rainfall-runoff modelling: A fuzzy set approach
    • Maskey, S., Guinot, V. & Price, R. K. 2004 Treatment of precipitation uncertainty in rainfall-runoff modelling: a fuzzy set approach. Advance in Water Resources 27, 889-898.
    • (2004) Advance in Water Resources , vol.27 , pp. 889-898
    • Maskey, S.1    Guinot, V.2    Price, R.K.3
  • 28
    • 0018468345 scopus 로고
    • A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
    • McKay, M. D., Conover, W. J. & Beckman, R. J. 1979 A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239-245.
    • (1979) Technometrics , vol.21 , pp. 239-245
    • McKay, M.D.1    Conover, W.J.2    Beckman, R.J.3
  • 29
    • 0026614965 scopus 로고
    • An improved-first-order reliability approach for assessing uncertainties in hydrologic modeling
    • Melching, C. S. 1992 An improved-first-order reliability approach for assessing uncertainties in hydrologic modeling. Journal of Hydrology 132, 157-177.
    • (1992) Journal of Hydrology , vol.132 , pp. 157-177
    • Melching, C.S.1
  • 30
    • 0030159380 scopus 로고    scopus 로고
    • Artificial neural networks as rainfall-runoff models
    • Minns, A. W. & Hall, M. J. 1996 Artificial neural networks as rainfall-runoff models. Hydrological Science Journal 41, 399-417.
    • (1996) Hydrological Science Journal , vol.41 , pp. 399-417
    • Minns, A.W.1    Hall, M.J.2
  • 31
    • 0004255908 scopus 로고    scopus 로고
    • McGraw-Hill, Singapore
    • Mitchell, T. 1997 Machine Learning. McGraw-Hill, Singapore, 414 pp.
    • (1997) Machine Learning , pp. 414
    • Mitchell, T.1
  • 32
    • 25844526502 scopus 로고    scopus 로고
    • Large sample behaviors of the generalized likelihood uncertainty estimation (GLUE) in assessing the uncertainty of rainfall-runoff simulations
    • Montanari, A. 2005 Large sample behaviors of the generalized likelihood uncertainty estimation (GLUE) in assessing the uncertainty of rainfall-runoff simulations. Water Resources Research 41, W08406.
    • (2005) Water Resources Research , vol.41
    • Montanari, A.1
  • 33
    • 2542555104 scopus 로고    scopus 로고
    • A stochastic approach for assessing the uncertainty of rainfall-runoff simulations
    • Montanari, A. & Brath, A. 2004 A stochastic approach for assessing the uncertainty of rainfall-runoff simulations. Water Resources Research 40, W01106.
    • (2004) Water Resources Research , vol.40
    • Montanari, A.1    Brath, A.2
  • 34
    • 0014776873 scopus 로고
    • River flow forecasting through conceptual models - Part i - ussion of principles
    • Nash, J. & Sutcliffe, J. 1970 River flow forecasting through conceptual models - Part I - A discussion of principles. Journal of Hydrology 10, 282-290.
    • (1970) Journal of Hydrology , vol.10 , pp. 282-290
    • Nash, J.1    Sutcliffe, J.2
  • 35
    • 33751112709 scopus 로고    scopus 로고
    • Decision tree for choosing an uncertainty analysis methodology: A wiki experiment
    • Pappenberger, F., Harvey, H., Beven, K., Hall, J. & Meadowcroft, I. 2006 Decision tree for choosing an uncertainty analysis methodology: a wiki experiment http://www.floodrisknet org.uk/methods http://www.floodrisk.net. Hydrological Processes 20, 3793-3798.
    • (2006) Hydrological Processes , vol.20 , pp. 3793-3798
    • Pappenberger, F.1    Harvey, H.2    Beven, K.3    Hall, J.4    Meadowcroft, I.5
  • 37
    • 33645987256 scopus 로고    scopus 로고
    • Machine learning approaches for estimation of prediction interval for the model output
    • Shrestha, D. L. & Solomatine, D. P. 2006 Machine learning approaches for estimation of prediction interval for the model output. Neural Networks 19, 225-235
    • (2006) Neural Networks , vol.19 , pp. 225-235
    • Shrestha, D.L.1    Solomatine, D.P.2
  • 39
    • 75749099728 scopus 로고    scopus 로고
    • A novel approach to parameter uncertainty analysis of hydrological models using neural networks
    • Shrestha, D. L., Kayastha, N. & Solomatine, D. P. 2009 A novel approach to parameter uncertainty analysis of hydrological models using neural networks. Hydrology and Earth System Sciences 13, 1235-1248
    • (2009) Hydrology and Earth System Sciences , vol.13 , pp. 1235-1248
    • Shrestha, D.L.1    Kayastha, N.2    Solomatine, D.P.3
  • 40
    • 0002391575 scopus 로고    scopus 로고
    • Two strategies of adaptive cluster covering with descent and their comparison to other algorithms
    • Solomatine, D. P. 1999 Two strategies of adaptive cluster covering with descent and their comparison to other algorithms. Journal of Global Optimization 14,55-78
    • (1999) Journal of Global Optimization , vol.14 , pp. 55-78
    • Solomatine, D.P.1
  • 41
    • 0002133630 scopus 로고    scopus 로고
    • Neural network approximation of a hydrodynamic model in optimizing reservoir operation
    • (A. Muller, ed.). Balkema, Rotterdam
    • Solomatine, D. P. & Torres, L. A. A. 1996 Neural network approximation of a hydrodynamic model in optimizing reservoir operation. In: Hydroinformatics '96 (A. Muller, ed.). Balkema, Rotterdam
    • (1996) Hydroinformatics '96
    • Solomatine, D.P.1    Torres, L.A.A.2
  • 42
    • 0037565156 scopus 로고    scopus 로고
    • Model trees as an alternative to neural networks in rainfall-runoff modelling
    • Solomatine, D. P. & Dulal, K N. 2003 Model trees as an alternative to neural networks in rainfall-runoff modelling. Hydrological Sciences Journal 48, 399-411
    • (2003) Hydrological Sciences Journal , vol.48 , pp. 399-411
    • Solomatine, D.P.1    Dulal, K.N.2
  • 43
    • 39449089195 scopus 로고    scopus 로고
    • Data-driven modelling: Some past experiences and new approaches
    • Solomatine, D. P. & Ostfeld, A. 2008 Data-driven modelling: some past experiences and new approaches. Journal of Hydroinformatics 10,3-22.
    • (2008) Journal of Hydroinformatics , vol.10 , pp. 3-22
    • Solomatine, D.P.1    Ostfeld, A.2
  • 44
    • 72149087074 scopus 로고    scopus 로고
    • A novel method to estimate model uncertainty using machine learning techniques
    • Solomatine, D. P. & Shrestha, D. L. 2009 A novel method to estimate model uncertainty using machine learning techniques. Water Resources Research 45, W00B11
    • (2009) Water Resources Research , vol.45
    • Solomatine, D.P.1    Shrestha, D.L.2
  • 45
    • 38549089135 scopus 로고    scopus 로고
    • Instance- based learning compared to other data-driven methods in hydrological forecasting
    • Solomatine, D. P., Maskey, M. & Shrestha, D. L. 2008 Instance- based learning compared to other data-driven methods in hydrological forecasting. Hydrological Processes 22, 275-287
    • (2008) Hydrological Processes , vol.22 , pp. 275-287
    • Solomatine, D.P.1    Maskey, M.2    Shrestha, D.L.3
  • 48
    • 0344568284 scopus 로고    scopus 로고
    • Uncertainty and reliability analysis
    • (L. W. Mays, ed.). McGraw-Hill, New York
    • Tung, Y.-K. 1996 Uncertainty and reliability analysis. In: Water Resources Handbook (L. W. Mays, ed.). McGraw-Hill, New York, 7.1-7.65.
    • (1996) Water Resources Handbook , vol.7 , Issue.1-7 , pp. 65
    • Tung, Y.-K.1
  • 49
    • 66049147497 scopus 로고    scopus 로고
    • A model conditional processor to assess predictive uncertainty in flood forecasting
    • Todini, E. 2008 A model conditional processor to assess predictive uncertainty in flood forecasting. Journal of River Basin Management 6, 123-137.
    • (2008) Journal of River Basin Management , vol.6 , pp. 123-137
    • Todini, E.1
  • 51
    • 14944365603 scopus 로고    scopus 로고
    • Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation
    • Vrugt, J. A., Diks, C., Gupta, H. V., Bouten, W. & Verstraten, J. M. 2005 Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation. Water Resources Research 41, W01017.
    • (2005) Water Resources Research , vol.41
    • Vrugt, J.A.1    Diks, C.2    Gupta, H.V.3    Bouten, W.4    Verstraten, J.M.5
  • 54
    • 37549062601 scopus 로고    scopus 로고
    • An empirical method to improve the prediction limits of the GLUE methodology in rainfallrunoff modeling
    • Xiong, L. & O'Connor, K. 2008 An empirical method to improve the prediction limits of the GLUE methodology in rainfallrunoff modeling. Journal of Hydrology 349, 115-124.
    • (2008) Journal of Hydrology , vol.349 , pp. 115-124
    • Xiong, L.1    O'Connor, K.2
  • 55
    • 0030162090 scopus 로고    scopus 로고
    • Automatic calibration of conceptual rainfall-runoff models: Sensitivity to calibration data
    • Yapo, P., Gupta, H. V. & Sorooshian, S. 1996 Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data. Journal of Hydrology 181,23-48.
    • (1996) Journal of Hydrology , vol.181 , pp. 23-48
    • Yapo, P.1    Gupta, H.V.2    Sorooshian, S.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.