Automatic rainfall recharge model induction by evolutionary computational intelligence

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Hong:2005:WRR,

• 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 = "T.Hong@gns.cri.nz",
• 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 = "http://www.agu.org/pubs/crossref/2005/2004WR003577.shtml",
• 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

Citations