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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2006)
Tan, P., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley, Reading (2006)
Arciszewski, T., De Jong, K.A.: Evolutionary computation in civil engineering: research frontiers. Civil and structural engineering computing, 161–184 (2001)
Flood, I.: Neural Networks in Civil Engineering. Civil and Structural Engineering Computing, 185–209 (2001)
Govindaraju, R.S., Rao, A.R.: Artificial Neural Networks in Hydrology. Water Science and Technology Library, vol. 36. Kluwer Academic Publishers, Dordrecht (2000)
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)
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)
Viessmann, W., Lewis, G.L., Knapp, J.W.: Introduction to Hydrology. Harper Collins, New York (1989)
Huber, W.C., Dickinson, R.E.: Storm Water Management Model, user’s manual, version 4. U.S. Envir. Protection Agency, Athens, Ga (1992)
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)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Koza, J.R., Bennet, F.H., Andre, D., Keane, M.: Genetic Programming III. Darwinian invention and problem solving. Morgan Kaufman Publishers, San Francisco (1999)
Garrote, L., Molina, M., Blasco, G.: Application of bayesian networks to Real-Time flood risk estimation. Geophysical Research Abstracts 5, 131–171 (2003)
Lingireddy, S., Brion, G.: Artificial Neural Networks in Water Supply Engineering. Editorial American Society of Civil Engineers (2005)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rabuñal, J.R., Puertas, J., Rivero, D., Fraga, I., Cea, L., Garrido, M. (2011). Genetic Programming for Prediction of Water Flow and Transport of Solids in a Basin. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) New Challenges on Bioinspired Applications. IWINAC 2011. Lecture Notes in Computer Science, vol 6687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21326-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-21326-7_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21325-0
Online ISBN: 978-3-642-21326-7
eBook Packages: Computer ScienceComputer Science (R0)