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Automatic rainfall recharge model induction by evolutionary computational intelligence
Author(s) -
Hong YoonSeok Timothy,
White Paul A.,
Scott David M.
Publication year - 2005
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2004wr003577
Subject(s) - groundwater recharge , evapotranspiration , genetic programming , water balance , hydrology (agriculture) , environmental science , geology , ecology , computer science , geotechnical engineering , aquifer , groundwater , biology , artificial intelligence
Genetic programming (GP) is used to develop models of rainfall recharge from observations of rainfall recharge and rainfall, calculated potential evapotranspiration (PET) and soil profile available water (PAW) at four sites over a 4 year period in Canterbury, New Zealand. This work demonstrates that the automatic model induction method is a useful development in modeling rainfall recharge. The five best performing models evolved by genetic programming show a highly nonlinear relationship between rainfall recharge and the independent variables. These models are dominated by a positive correlation with rainfall, a negative correlation with the square of PET, and a negative correlation with PAW. The best performing GP models are more reliable than a soil water balance model at predicting rainfall recharge when rainfall recharge is observed in the late spring, summer, and early autumn periods. The “best” GP model provides estimates of cumulative sums of rainfall recharge that are closer than a soil water balance model to observations at all four sites.