Automatic rainfall recharge model induction by evolutionary computational intelligence

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Yoon-Seok Timothy Hong and Paul A. White and 
                 David M. Scott",
  title =        "Automatic rainfall recharge model induction by
                 evolutionary computational intelligence",
  journal =      "Water Resources Research",
  year =         "2005",
  volume =       "41",
  number =       "W08422",
  email =        "",
  keywords =     "genetic algorithms, genetic programming, automatic
                 rainfall recharge model induction, Canterbury Plains,
                 evolutionary computational intelligence, New Zealand,
                 soil moisture balance model, 0555 Computational
                 Geophysics: Neural networks, fuzzy logic, machine
                 learning; 1805 Hydrology: Computational hydrology; 1816
                 Hydrology: Estimation and forecasting; 1829 Hydrology:
                 Groundwater hydrology; 1847 Hydrology: Modelling",
  URL =          "",
  DOI =          "doi:10.1029/2004WR003577",
  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.",

Genetic Programming entries for Yoon-Seok Hong Paul A White David M Scott