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GENETIC PROGRAMMING: A NEW PARADIGM IN RAINFALL RUNOFF MODELING 1
Author(s) -
Liong ShieYui,
Gautam Tirtha Raj,
Khu Soon Thiam,
Babovic Vladan,
Keijzer Maarten,
Muttil Nitin
Publication year - 2002
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.2002.tb00991.x
Subject(s) - genetic programming , symbolic regression , surface runoff , storm , drainage basin , hydrology (agriculture) , regression , runoff model , regression analysis , runoff curve number , computer science , mean squared error , correlation coefficient , drainage , relation (database) , environmental science , statistics , mathematics , meteorology , data mining , geology , ecology , artificial intelligence , geography , geotechnical engineering , biology , cartography
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 km 2 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.