Improving Daily Reservoir Inflow Forecasts with Model Combination
Publication: Journal of Hydrologic Engineering
Volume 10, Issue 2
Abstract
A major issue in real-time management of water resources is the need for accurate and reliable hydrologic forecasts at least 24 or ahead. An experiment on improving the accuracy of a conceptual hydrologic model used for daily reservoir inflow forecasting, by resorting to model combination, is presented. A robust weighted-average method is used to take advantage of three dynamically different models: a nearest-neighbor model, a conceptual model, and an artificial neural network model. At each time step, the output of each of these three models is computed, and either the absolute best result is considered or the competitive results are combined using the improved weighted-average method. The latter approach has shown a significant forecast improvement for up to -ahead prediction. Moreover, it is found that with the model combination, there is no need for postcorrection of the conceptual model forecasts. It is also found that the prediction accuracy is mainly driven by the nearest-neighbor method for the -ahead forecasts, and relatively by each model afterwards. However, none of the three models appears significantly better than the combined model approach, whatever the prediction lead time.
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Acknowledgments
This work was made possible through a joint grant from Hydro-Québec and the National Science and Engineering Research Council of Canada (NSERC). This work has benefited substantially from the authors' discussion with Dr. R. Roy and Dr. M. Durocher during the development of the NNM model. The authors are also grateful to C. Mathieu for preparing the map.
References
Abrahart, R. J., and See, L. (2002). “Multi-model data fusion for river flow forecasting: An evaluation of six alternative methods based on two contrasting catchments.” Hydrology Earth Syst. Sci. 6(4), 655–670.
Bates, J. M., and Granger, C. W. J. (1969). “The combination of forecasts.” Oper. Res. Q., 20, 451–468.
Coulibaly, P., Anctil, F., and Bobée, B. (1999). “Prévision hydrologique par réseaux de neurones artificiels: état de l’art.” Can. J. Civ. Eng., 26(3), 293–304.
Coulibaly, P., Anctil, F., and Bobée, B. (2000). “Daily reservoir inflow forecasting using artificial neural networks with stopped training approach.” J. Hydrol., 230(3–4), 244–257.
Dickinson, J. P. (1973). “Some statistical results in the combination of forecasts.” Oper. Res. Q., 24(2), 253–260.
Fisher, D. H., and Schlimmer, J. C. (1988). “Concept simplification and predictive accuracy.” Proc., 5th Int. Conf. Machine Learning, 22–28.
Fortin, V. (1999). Le modèle météo-apport HSAMI: Historique, théorie, et application. Rapport de recherche, Institut de Recherche d’Hydro-Québec (IREQ), Québec.
Galeati, G. (1990). “A comparison of parametric and non-parametric methods for runoff forecasting.” Hydrol. Sci. J., 35(1), 79–94.
Hagan, M. T., and Menhaj, M. B. (1994). “Training feedforward networks with the Marquardt algorithm.” IEEE Trans. Neural Netw., 5(6), 989–993.
Karlsson, M., and Yakowitz, S. (1987). “Nearest-neighbor methods for nonparametric rainfall-runoff forecasting.” Water Resour. Res., 23(7), 1300–1308.
Lebowitz, M. (1987). “Experiments with universal concept formation.” Mach. Learn., 2, 103–138.
Mack, Y. P., and Rosenblatt, M. (1979). “Multivariate -nearest neighbor density estimates.” J. Multivariate Anal., 9, 1–15.
Maier, H. R., and Dandy, G. C. (2000). “Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications.” Environ. Model. Softw., 15, 101–124.
Newbold, P., and Granger, C. W. J. (1974). “Experience with forecasting univariate time series and the combination of forecasts.” J. R. Stat. Soc. Ser. A. Gen., 137, 131–149.
Shamseldin, A. Y., and O’Connor, K. M. (1996). “A nearest neighbor linear perturbation model for river flow forecasting.” J. Hydrol., 179, 353–375.
Shamseldin, A. Y., O’Connor, K. M., and Liang, G. C. (1997). “Methods for combining the output of different rainfall-runoff models.” J. Hydrol., 197, 203–229.
Singh, V. P. (1995). Computer models of watershed hydrology. Water Resource Publications, Highlands Ranch, Colo.
Singh, V. P., and Woolhiser, D. A. (2002). “Mathematical modeling of watershed hydrology.” J. Hydrologic Eng., 7(4), 270–292.
Toth, E., Brath, A., and Montanari, A. (2000). “Comparison of short-term rainfall prediction models for real-time flood forecasting.” Acta Radiol., 239, 132–147.
Winkler, R. L. (1989). “Combining forecast: a philosophical basis and some current issues.” Int. J. Forecast., 5(4), 605–609.
Winkler, R. L., and Makridakis, S. (1983). “The combination of forecasts.” J. R. Stat. Soc. A., 146, 150–157.
World Meteorological Organization (WMO). (1992). “Simulated real-time intercomparison of hydrological models.” Operational Hydrology Rep. 38, WMO No. 779, Geneva.
Xiong, L., Shamseldin, A. Y., and O’Connor, K. M., (2001). “A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi-Sugeno fuzzy system.” J. Hydrol., 245, 196–217.
Yakowitz, S. (1987). “Nearest neighbor methods for time series analysis.” J. Time Ser. Anal., 8(2), 235–247.
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Published In
Journal of Hydrologic Engineering
Volume 10 • Issue 2 • March 2005
Pages: 91 - 99
Copyright
© 2005 ASCE.
History
Received: Nov 12, 2003
Accepted: Jun 4, 2004
Published online: Mar 1, 2005
Published in print: Mar 2005
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