Combining Rainfall-Runoff Model Outputs for Improving Ensemble Streamflow Prediction

This study reviewed various combining methods that have been commonly used in economic forecasting, and examined their applicability in hydrologic forecasting. The following combining methods were investigated: The simple average, constant coefficient regression, switching regression, sum of squared error, and artificial neural network combining methods. Each method combines ensemble streamflow prediction (ESP) scenarios of the existing rainfall-runoff model, TANK, those of the new rainfall-runoff model that has been developed using an ensemble neural network for forecasting the monthly inflow to the Daecheong multipurpose dam in Korea. In addition to the combining, the ESP scenarios were adjusted using correction methods, such as optimal linear and artificial neural network correction methods. Among the tested combining methods, sum of squared error (SSE), a combining method using time-varying weights, performed best with respect to the root mean square error. When SSE was coupled with optimal linear cor...

[1]  A. Shamseldin,et al.  Methods for combining the outputs of different rainfall–runoff models , 1997 .

[2]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[3]  H. V. Dijk,et al.  Combined forecasts from linear and nonlinear time series models , 1999 .

[4]  A. H. Murphy,et al.  Screening probability forecasts: contrasts between choosing and combining , 1995 .

[5]  Keith Beven,et al.  Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology , 2001 .

[6]  Thomas E. Croley,et al.  Using Meteorology Probability Forecasts in Operational Hydrology , 2000 .

[7]  Roman Krzysztofowicz,et al.  Probabilistic and ensemble forecasting , 2001 .

[8]  M. Perrone.  Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization , 1993 .

[9]  Young-Oh Kim,et al.  Rainfall‐runoff models using artificial neural networks for ensemble streamflow prediction , 2005 .

[10]  Roman Krzysztofowicz,et al.  Bayesian theory of probabilistic forecasting via deterministic hydrologic model , 1999 .

[11]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[12]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[13]  C. Granger,et al.  Improved methods of combining forecasts , 1984 .

[14]  Paul Goodwin,et al.  Correct or combine? Mechanically integrating judgmental forecasts with statistical methods , 2000 .

[15]  R. L. Winkler,et al.  Averages of Forecasts: Some Empirical Results , 1983 .

[16]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[17]  Alex J. Cannon,et al.  Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models , 2002 .

[18]  D. Jeong,et al.  Improving Water Supply Outlook in Korea with Ensemble Streamflow Prediction , 2001 .

[19]  Hui Zou,et al.  Combining time series models for forecasting , 2004, International Journal of Forecasting.

[20]  A. Shamseldin,et al.  A real-time combination method for the outputs of different rainfall-runoff models , 1999 .

[21]  Mark J. Kamstra,et al.  Forecast combining with neural networks , 1996 .

[22]  Keith W. Hipel,et al.  Combining Hydrologic Forecasts , 1987 .

[23]  R. Clemen.  Combining forecasts: A review and annotated bibliography , 1989 .

[24]  Heejoon Kang.  Unstable Weights in the Combination of Forecasts , 1986 .

[25]  Timo Teräsvirta,et al.  The combination of forecasts using changing weights , 1994 .

[26]  J. Scott Armstrong,et al.  Combining forecasts: The end of the beginning or the beginning of the end? , 1989 .

[27]  C. Granger,et al.  Forecasting Economic Time Series. , 1988 .

[28]  Stephen Figlewski,et al.  Optimal Aggregation of Money Supply Forecasts: Accuracy, Profitability and Market Efficiency , 1983 .

[29]  Daniel S. Wilks,et al.  Statistical Methods in the Atmospheric Sciences: An Introduction , 1995 .

[30]  C. B. Tilanus,et al.  Applied Economic Forecasting , 1966 .

[31]  Derek W. Bunn,et al.  Investigating improvements in the accuracy of prediction intervals for combinations of forecasts: A simulation study , 1999 .

[32]  Gerald N. Day,et al.  Extended Streamflow Forecasting Using NWSRFS , 1985 .

[33]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[34]  R. L. Winkler,et al.  The Combination of Forecasts , 1983 .