Estimating Saturated Hydraulic Conductivity Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{KambanParasuraman09282007,
  author =       "Kamban Parasuraman and Amin Elshorbagy and 
                 Bing Cheng Si",
  title =        "Estimating Saturated Hydraulic Conductivity Using
                 Genetic Programming",
  journal =      "Soil Science Society of America Journal",
  year =         "2007",
  volume =       "71",
  number =       "6",
  pages =        "1676--1684",
  keywords =     "genetic algorithms, genetic programming",
  broken =       "http://soil.scijournals.org/cgi/content/abstract/soilsci;71/6/1676",
  URL =          "http://soil.scijournals.org/cgi/reprint/soilsci;71/6/1676.pdf",
  DOI =          "doi:10.2136/sssaj2006.0396",
  abstract =     "Saturated hydraulic conductivity (Ks) is one of the
                 key parameters in modelling solute and water movement
                 in the vadose zone. Field and laboratory measurement of
                 Ks is time consuming, and hence is not practical for
                 characterising the large spatial and temporal
                 variability of Ks. As an alternative to direct
                 measurements, pedotransfer functions (PTFs), which
                 estimate Ks from readily available soil data, are being
                 widely adopted. This study explores the utility of a
                 promising data-driven method, namely, genetic
                 programming (GP), to develop PTFs for estimating Ks
                 from sand, silt, and clay contents and bulk density
                 (Db). A data set from the Unsaturated Soil Hydraulic
                 Database (UNSODA) was considered in this study. The
                 performance of the GP models were compared with the
                 neural networks (NNs) model, as it is the most widely
                 adopted method for developing PTFs. The uncertainty of
                 the PTFs was evaluated by combining the GP and the NN
                 models, using the nonparametric bootstrap method.
                 Results from the study indicate that GP appears to be a
                 promising tool for developing PTFs for estimating Ks.
                 The better performance of the GP model may be
                 attributed to the ability of GP to optimise both the
                 model structure and its parameters in unison. For the
                 PTFs developed using GP, the uncertainty due to model
                 structure is shown to be more than the uncertainty due
                 to model parameters. Moreover, the results indicate
                 that it is difficult, if not impossible, to achieve
                 better prediction and less uncertainty
                 simultaneously.",
}

Genetic Programming entries for Kamban Parasuraman Amin Elshorbagy Bing Cheng Si

Citations