Abstract
Streamflow forecasting is a fundamental tool in water resource studies. If information on the nature of the inflow is determinable in advance, then a given reservoir can be operated by some decision rule to minimize downstream flood damage and maximize the generated power with low costs. However, traditional methods such as linear time series models do not model the series properly, ignoring its dynamical behavior. This paper provides a method based on the Reservoir Computing (RC) technique combined with trend information extracted from the series for short-term streamflow forecasting. The model was tested in five hydroelectric plants located in different river basins in Brazil. Experimental results show that the proposed method is able to achieve better generalization performance than the traditional methods.
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References
Brooks, K., Ffolliott, P., Gregersen, H., DeBani, L.: Hydrology and the Management of Watersheds (2003)
Box, G., Jenkins, G.: Time Series Analysis - Forecasting and Control (1976)
Haykin, S.: Neural Networks: A Comprehensive Foundation (1998)
Kişi, O.: Evolutionary neural networks for monthly pan evaporation modeling. J. Hydrol. 498, 36–45 (2013)
Chang, F.J., Chen, P.A., Lu, Y.-R., Huang, E., Chang, K.Y.: Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control. J. Hydrol. 517, 836–846 (2014)
He, X., Guan, H., Qin, J.: A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall. J. Hydrol. 527, 88–100 (2015)
Verstraeten, D.: Reservoir Computing: computation with dynamical systems. Ph.D. Dissertation (2009)
Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3), 127–149 (2009)
Joseph, J.F., Falcon, H.E., Sharif, H.O.: Hydrologic trends and correlations in south texas river basins: 1950–2009. J. Hydrol. Eng. 18(2), 1653–1662 (2013)
Zhang, G., Qi, M.: Neural network forecasting for seasonal and trend time series. European Journal of Operational Research 160(2), 501–514 (2005)
Chang, F.J., Lo, Y.C., Chen, P.A., Chang, L.C., Shieh, M.C.: Multi-step-ahead reservoir inflow forecasting by artificial intelligence techniques. In: Tweedale, J., Jain, L.C., Watada, J., Howlett, R.J. (eds.) Knowledge-Based Information Systems in Practice. SIST, vol. 30, pp. 229–242. Springer, Heidelberg (2015)
Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks. Technical report, German National Research Center for Information Technology (2001)
Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14(11), 2531–2560 (2002)
Marengo, J.A., Tomasella, J.: Trends in streamflow and rainfall in tropical South America: Amazonia, eastern Brazil, and northwestern Peru. J. Geophys. Res. 103(2), 1775–1783 (1998)
Moura, L.Z.: Evaluation of monotonic trends for streamflow in austral Amazon, Brazil: a case study for the Xingu and Tapajós rivers. In: Proceedings of the International Association of Hydrological Sciences, vol. 371, pp. 125–130, June 2015
Baldwin, J.F., Martin, T.P., Rossiter, J.: Time series modelling and prediction using fuzzy trend information. In: Proceedings of the 5th International Conference on Soft Computing and Information Intelligent Systems, pp. 499–502 (1998)
Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting, 2nd edn. Springer, New York (2002)
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Bezerra, S.G.T.A., de Andrade, C.B., Valença, M.J.S. (2016). Using Reservoir Computing and Trend Information for Short-Term Streamflow Forecasting. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_37
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DOI: https://doi.org/10.1007/978-3-319-44781-0_37
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