21 Rainfall-Runoff Modeling Based on Genetic Programming
Part 2. Hydroinformatics
Published Online: 15 APR 2006
Copyright © 2005 John Wiley & Sons, Ltd
Encyclopedia of Hydrological Sciences
How to Cite
Babovic, V. and Keijzer, M. 2006. Rainfall-Runoff Modeling Based on Genetic Programming. Encyclopedia of Hydrological Sciences. 2:21.
- Published Online: 15 APR 2006
The runoff formation process is believed to be highly nonlinear, time varying, spatially distributed, and not easily described by simple models. Considerable time and effort has been directed to model this process, and many hydrologic models have been built specifically for this purpose. All of them, however, require significant amounts of data for their respective calibration and validation. Using physical models raises issues of collecting the appropriate data with sufficient accuracy. In most cases, it is difficult to collect all the data necessary for such a model.
By using data-driven models such as genetic programming (GP), one can attempt to model runoff on the basis of available hydrometeorological data. This work addresses the use of GP for creating rainfall-runoff (R-R) models both on the basis of data alone, as well as in combination with conceptual models (i.e taking advantage of knowledge about the problem domain).
- genetic programming;
- symbolic regression;
- empirical equations;