Please use this identifier to cite or link to this item:
Title: Genetic Programming: A new paradigm in rainfall runoff modeling
Authors: Liong, S.-Y. 
Gautam, T.R. 
Soon, T.K. 
Babovic, V. 
Keijzer, M.
Muttil, N. 
Keywords: Evolutionary algorithms
Genetic programming
Rainfall-runoff relationships
Runoff forecasting
Issue Date: 2002
Citation: Liong, S.-Y.,Gautam, T.R.,Soon, T.K.,Babovic, V.,Keijzer, M.,Muttil, N. (2002). Genetic Programming: A new paradigm in rainfall runoff modeling. Journal of the American Water Resources Association 38 (3) : 705-718. ScholarBank@NUS Repository.
Abstract: Genetic Programming (GP) is a domain-independent evolutionary programming technique that evolves computer programs to solve, or approximately solve, problems. To verify GP's capability, a simple example with known relation in the area of symbolic regression, is considered first. GP is then utilized as a flow forecasting tool. A catchment in Singapore with a drainage area of about 6 km2 is considered in this study. Six storms of different intensities and durations are used to train GP and then verify the trained GP. Analysis of the GP induced rainfall and runoff relationship shows that the cause and effect relationship between rainfall and runoff is consistent with the hydrologic process. The result shows that the runoff prediction accuracy of symbolic regression based models, measured in terms of root mean square error and correlation coefficient, is reasonably high. Thus, GP induced rainfall runoff relationships can be a viable alternative to traditional rainfall runoff models.
Source Title: Journal of the American Water Resources Association
ISSN: 1093474X
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Oct 12, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.