Abstract
This article presents an approach of data partitioning using specialist knowledge incorporated to intelligent solutions for river flow prediction. The main idea is to train the processes through a hybrid systems, neural networks and fuzzy, characterizing its physical process. As a case study, the results obtained with this models from three basins, Três Marias, Tucuruí and Foz do Areia, all situated in Brazil, are investigated.
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Almeida, P.E.M., Evsukoff, A.G.: Intelligent Systems: Fundamentals and Applications (Sistemas Inteligentes: Fundamentos e Aplicações). cap. Fuzzy Systems, Manole, Barueru, São Paulo (2005)
Birikundavyi, S., Labib, R., Trung, H.T., Rousselle, J.: Performance of neural networks in daily streamflow Forecasting. J. Hydrol. Engg., ASCE 7(5), 392–398 (2002)
Braga, A.P., Carvalho, A.C., Ludermir, T.B.: Artificial Neural Networks: Theory and Applications (Redes Neurais Artificiais: Teoria e Aplicações), 262 p. Rio de Janeiro, Livro Técnico e Científico (2000)
Campolo, M., Andreussi, P., Soldati, A.: River Flood Forecasting with Neural Network Model. Wat. Resour. Res. 35(4), 1191–1197 (1999)
Dawson, D.W., Wilby, R.: An artificial neural network approach to rainfall runoff modeling. Hydrol. Sci. J. 43(1), 47–65 (1998)
Fenicia, F., Savenije, H.H.G., Matgen, P., Pfister, L.: A comparison of alternative multiobjective calibration strategies for hydrological modeling. Water Resources Research 43(3) (2007)
Corzo, G., Solomatine, D.: Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting. Neural Networks 20, 528–536 (2007)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn., p. 842. Prentice Hall, Englewood Cliffs (1998)
Hsu, K.-L., Gupta, H.V., Sorooshian, S.: Artificial Neural Network Modeling of the Rainfall-Runoff Process. Wat. Resour. Res. 31(10), 2517–2530 (1995)
Kartalopoulos, S.V.: Understanding Neural Networks and Fuzzy Logic. IEEE Press, Los Alamitos (1996)
Kosko, B.: Neural Networks and Fuzzy Systems. Prentice-Hall, Englewood Cliffs (1992)
Jain, A., Indurthy, S.K.V.P.: Comparative analysis of event based rainfall-runoff modeling techniques-deterministic, statistical, and artificial neural networks. J. Hydrol. Engg., ASCE 8(2), 1–6 (2003)
Minns, A.W., Hall, M.J.: Artificial neural networks as rainfall runoff models. Hydrol. Sci. Jour. 41(3), 399–417 (1996)
Reyes, C.A.P.: Coevolutionary Fuzzy Modeling. In: Peña Reyes, C.A. (ed.) Coevolutionary Fuzzy Modeling. LNCS, vol. 3204, pp. 51–69. Springer, Heidelberg (2004)
Sajikumar, N., Thandaveswara, B.S.: A non-linear rainfall-runoff model using an artificial neural network. J. Hydrol. 216, 32–55 (1999)
Sato, et al.: Learning chaotic dynamics by recurrent neural networks. In: Proceeding of the International Conference on Fuzzy Logic and Neural Nets, Iizuka, pp. 601–604 (1990)
Shamseldin, A.Y.: Application of a neural network technique to rainfall-runoff modeling. J. Hydrol. 199, 272–294 (1997)
Smith, J., Eli, R.N.: Neural Network Models of the Rainfall Runoff Process. ASCE Jour. Wat. Res. Plng. Mgmt. 121, 499–508 (1995)
Tokar, A.S., Markus, M.: Precipitation Runoff Modeling Using Artificial Neural Network and Conceptual models. J. Hydrol. Engg., ASCE 5(2), 156–161 (2000)
Valença, M.J.S.: Applying Neural Networks: a complete guide (Aplicando Redes Neurais: um guia completo), 264 p. Livro Rápido, Olinda-PE (2005)
Valença, M.J.S.: Fundamentals of Neural Networks: examples in Java (Fundamentos das Redes Neurais: exemplos em Java), 382 p. Livro Rápido, Olinda-PE (2007)
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Valença, I., Ludermir, T. (2009). Hybrid Systems for River Flood Forecasting Using MLP, SOM and Fuzzy Systems. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_58
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DOI: https://doi.org/10.1007/978-3-642-04274-4_58
Publisher Name: Springer, Berlin, Heidelberg
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