Advertisement

Genetic Programming for Prediction of Water Flow and Transport of Solids in a Basin

  • Juan R. Rabuñal
  • Jerónimo Puertas
  • Daniel Rivero
  • Ignacio Fraga
  • Luis Cea
  • Marta Garrido
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)

Abstract

One of the applications of Data Mining is the extraction of knowledge from time series [1][2]. The Evolutionary Computation (EC) and the Artificial Neural Networks (ANNs) have proved to be suitable in Data Mining for handling this type of series [3] [4]. This paper presents the use of Genetic Programming (GP) for the prediction of time series in the field of Civil Engineering where the predictive structure does not follow the classic paradigms. In this specific case, the GP technique is applied to two phenomenon that models the process where, for a specific area, the fallen rain concentrates and flows on the surface, and later from the water flows is predicted the solids transport. In this article it is shown the Genetic Programming technique use for the water flows and the solids transport prediction. It is achieved good results both in the water flow prediction as in the solids transport prediction.

Keywords

Evolutionary Computation Genetic Programming Civil Engineering Hydrology 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2006)zbMATHGoogle Scholar
  2. 2.
    Tan, P., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley, Reading (2006)Google Scholar
  3. 3.
    Arciszewski, T., De Jong, K.A.: Evolutionary computation in civil engineering: research frontiers. Civil and structural engineering computing, 161–184 (2001)Google Scholar
  4. 4.
    Flood, I.: Neural Networks in Civil Engineering. Civil and Structural Engineering Computing, 185–209 (2001)Google Scholar
  5. 5.
    Govindaraju, R.S., Rao, A.R.: Artificial Neural Networks in Hydrology. Water Science and Technology Library, vol. 36. Kluwer Academic Publishers, Dordrecht (2000)CrossRefGoogle Scholar
  6. 6.
    Miguélez, M., Puertas, J., Rabuñal, J.R.: Artificial neural networks in urban runoff forecast. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5517, pp. 1192–1199. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Freire, A., Aguiar, V., Rabual, J.R., Garrido, M.: Genetic Algorithm based on Differential Evolution with variable length. Runoff prediction on an artificial basin. In: International Conference on Evolutionary Computation, ICEC (2010)Google Scholar
  8. 8.
    Viessmann, W., Lewis, G.L., Knapp, J.W.: Introduction to Hydrology. Harper Collins, New York (1989)Google Scholar
  9. 9.
    Huber, W.C., Dickinson, R.E.: Storm Water Management Model, user’s manual, version 4. U.S. Envir. Protection Agency, Athens, Ga (1992)Google Scholar
  10. 10.
    Darwin, C.: On the origin of species by means of natural selection or the preservation of favoured races in the struggle for life. Cambridge University Press, Cambridge (1859)CrossRefGoogle Scholar
  11. 11.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  12. 12.
    Koza, J.R., Bennet, F.H., Andre, D., Keane, M.: Genetic Programming III. Darwinian invention and problem solving. Morgan Kaufman Publishers, San Francisco (1999)zbMATHGoogle Scholar
  13. 13.
    Garrote, L., Molina, M., Blasco, G.: Application of bayesian networks to Real-Time flood risk estimation. Geophysical Research Abstracts 5, 131–171 (2003)Google Scholar
  14. 14.
    Lingireddy, S., Brion, G.: Artificial Neural Networks in Water Supply Engineering. Editorial American Society of Civil Engineers (2005)Google Scholar
  15. 15.
    Wu Jy, S., Han, J., Annambhotla, S., Bryant, S.: Artificial Neural Networks for Forecasting Watershed Runoff and Stream Flows. Journal of Hydrologic Engineering 10(3), 216–222 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Juan R. Rabuñal
    • 1
    • 2
  • Jerónimo Puertas
    • 2
  • Daniel Rivero
    • 1
  • Ignacio Fraga
    • 2
  • Luis Cea
    • 2
  • Marta Garrido
    • 2
  1. 1.Department of Information and Communication Technologies, Facultad de InformáticaUniversity of CoruñaA CoruñaSpain
  2. 2.Centre of Technological Innovations in Construction and Civil Engineering (CITEEC)University of CoruñaA CoruñaSpain

Personalised recommendations