Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/65650
Title: Genetic programming and its application in real-time runoff forecasting
Authors: Khu, S.T.
Liong, S.-Y. 
Babovic, V.
Madsen, H.
Muttil, N. 
Keywords: Evolutionary algorithms
Genetic programming
Rainfall-runoff
Real-time forecasting
Regression
Updating
Issue Date: Apr-2001
Source: Khu, S.T.,Liong, S.-Y.,Babovic, V.,Madsen, H.,Muttil, N. (2001-04). Genetic programming and its application in real-time runoff forecasting. Journal of the American Water Resources Association 37 (2) : 439-451. ScholarBank@NUS Repository.
Abstract: Genetic programming (GP), a relatively new evolutionary technique, is demonstrated in this study to evolve codes for the solution of problems. First, a simple example in the area of symbolic regression is considered. GP is then applied to real-time runoff forecasting for the Orgeval catchment in France. In this study, GP functions as an error updating scheme to complement a rainfall-runoff model, MIKE11/NAM. Hourly runoff forecasts of different updating intervals are performed for forecast horizons of up to nine hours. The results show that the proposed updating scheme is able to predict the runoff quite accurately for all updating intervals considered and particularly for updating intervals not exceeding the time of concentration of the catchment. The results are also compared with those of an earlier study, by the World Meteorological Organization, in which autoregression and Kalman filter were used as the updating methods. Comparisons show that GP is a better updating tool for real-time flow forecasting. Another important finding from this study is that nondimensionalizing the variables enhances the symbolic regression process significantly.
Source Title: Journal of the American Water Resources Association
URI: http://scholarbank.nus.edu.sg/handle/10635/65650
ISSN: 1093474X
Appears in Collections:Staff Publications

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