Estimating Saturated Hydraulic Conductivity Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@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

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