%0 Journal Article %1 Hong:2005:WRR %A Hong, Yoon-Seok Timothy %A White, Paul A. %A Scott, David M. %D 2005 %J Water Resources Research %K 0555 1805 1816 1829 1847 Canterbury Computational Estimation Geophysics: Groundwater Hydrology: Modelling Neural New Plains, Zealand, algorithms, and automatic balance computational evolutionary forecasting; fuzzy genetic hydrology; induction, intelligence, learning; logic, machine model model, moisture networks, programming, rainfall recharge soil %N W08422 %R doi:10.1029/2004WR003577 %T Automatic rainfall recharge model induction by evolutionary computational intelligence %U http://www.agu.org/pubs/crossref/2005/2004WR003577.shtml %V 41 %X 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.

@article{Hong:2005:WRR, abstract = {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.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Hong, Yoon-Seok Timothy and White, Paul A. and Scott, David M.}, biburl = {https://www.bibsonomy.org/bibtex/2906f90878ca74591f0a1e7fe79a8bb37/brazovayeye}, doi = {doi:10.1029/2004WR003577}, email = {T.Hong@gns.cri.nz}, interhash = {5ba84f7ed0b6859ba5815c0a29a537ee}, intrahash = {906f90878ca74591f0a1e7fe79a8bb37}, journal = {Water Resources Research}, keywords = {0555 1805 1816 1829 1847 Canterbury Computational Estimation Geophysics: Groundwater Hydrology: Modelling Neural New Plains, Zealand, algorithms, and automatic balance computational evolutionary forecasting; fuzzy genetic hydrology; induction, intelligence, learning; logic, machine model model, moisture networks, programming, rainfall recharge soil}, number = {W08422}, timestamp = {2008-06-19T17:41:42.000+0200}, title = {Automatic rainfall recharge model induction by evolutionary computational intelligence}, url = {http://www.agu.org/pubs/crossref/2005/2004WR003577.shtml}, volume = 41, year = 2005 }