Genetic Programming for Prediction of Water Flow and Transport of Solids in a Basin
One of the applications of Data Mining is the extraction of knowledge from time series . The Evolutionary Computation (EC) and the Artificial Neural Networks (ANNs) have proved to be suitable in Data Mining for handling this type of series  . 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.
KeywordsEvolutionary Computation Genetic Programming Civil Engineering Hydrology
Unable to display preview. Download preview PDF.
- 2.Tan, P., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley, Reading (2006)Google Scholar
- 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.Flood, I.: Neural Networks in Civil Engineering. Civil and Structural Engineering Computing, 185–209 (2001)Google Scholar
- 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.Viessmann, W., Lewis, G.L., Knapp, J.W.: Introduction to Hydrology. Harper Collins, New York (1989)Google Scholar
- 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
- 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.Lingireddy, S., Brion, G.: Artificial Neural Networks in Water Supply Engineering. Editorial American Society of Civil Engineers (2005)Google Scholar