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International Conference on Artificial Neural Networks

ICANN 2009: Artificial Neural Networks – ICANN 2009 pp 557–566Cite as

Hybrid Systems for River Flood Forecasting Using MLP, SOM and Fuzzy Systems

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5768))

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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References

  1. 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)

    Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. 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)

    Google Scholar 

  4. Campolo, M., Andreussi, P., Soldati, A.: River Flood Forecasting with Neural Network Model. Wat. Resour. Res. 35(4), 1191–1197 (1999)

    Article  Google Scholar 

  5. Dawson, D.W., Wilby, R.: An artificial neural network approach to rainfall runoff modeling. Hydrol. Sci. J. 43(1), 47–65 (1998)

    Article  Google Scholar 

  6. 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)

    Google Scholar 

  7. Corzo, G., Solomatine, D.: Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting. Neural Networks 20, 528–536 (2007)

    Article  Google Scholar 

  8. Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn., p. 842. Prentice Hall, Englewood Cliffs (1998)

    Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. Kartalopoulos, S.V.: Understanding Neural Networks and Fuzzy Logic. IEEE Press, Los Alamitos (1996)

    MATH  Google Scholar 

  11. Kosko, B.: Neural Networks and Fuzzy Systems. Prentice-Hall, Englewood Cliffs (1992)

    MATH  Google Scholar 

  12. 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)

    Google Scholar 

  13. Minns, A.W., Hall, M.J.: Artificial neural networks as rainfall runoff models. Hydrol. Sci. Jour. 41(3), 399–417 (1996)

    Article  Google Scholar 

  14. 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)

    Chapter  Google Scholar 

  15. Sajikumar, N., Thandaveswara, B.S.: A non-linear rainfall-runoff model using an artificial neural network. J. Hydrol. 216, 32–55 (1999)

    Article  Google Scholar 

  16. 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)

    Google Scholar 

  17. Shamseldin, A.Y.: Application of a neural network technique to rainfall-runoff modeling. J. Hydrol. 199, 272–294 (1997)

    Article  Google Scholar 

  18. Smith, J., Eli, R.N.: Neural Network Models of the Rainfall Runoff Process. ASCE Jour. Wat. Res. Plng. Mgmt. 121, 499–508 (1995)

    Article  Google Scholar 

  19. Tokar, A.S., Markus, M.: Precipitation Runoff Modeling Using Artificial Neural Network and Conceptual models. J. Hydrol. Engg., ASCE 5(2), 156–161 (2000)

    Article  Google Scholar 

  20. Valença, M.J.S.: Applying Neural Networks: a complete guide (Aplicando Redes Neurais: um guia completo), 264 p. Livro Rápido, Olinda-PE (2005)

    Google Scholar 

  21. 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)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Federal University of Pernambuco, C.P 7851, 50.732-970, Recife, PE, Brazil

    Ivna Valença & Teresa Ludermir

Authors
  1. Ivna Valença
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  2. Teresa Ludermir
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Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milano, Italy

    Cesare Alippi

  2. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Street, 1678, Nicosia, Cyprus

    Marios Polycarpou , Christos Panayiotou  & Georgios Ellinas ,  & 

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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

  • Print ISBN: 978-3-642-04273-7

  • Online ISBN: 978-3-642-04274-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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