A Computational Intelligence-Based Genetic Programming Approach for the Simulation of Soil Water Retention Curves

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@Article{Garg:2014:TPM,
  author =       "Ankit Garg and Akhil Garg and K. Tai and 
                 S. Barontini and Alexia Stokes",
  title =        "A Computational Intelligence-Based Genetic Programming
                 Approach for the Simulation of Soil Water Retention
                 Curves",
  journal =      "Transport in Porous Media",
  year =         "2014",
  volume =       "103",
  number =       "3",
  pages =        "497--513",
  keywords =     "genetic algorithms, genetic programming, multi-gene
                 genetic programming, soil water retention curves,
                 swelling soils, enveloppe potential, environmental
                 sciences/biodiversity and ecology",
  ISSN =         "1573-1634",
  URL =          "https://hal.archives-ouvertes.fr/hal-01268778",
  URL =          "http://dx.doi.org/10.1007/s11242-014-0313-8",
  DOI =          "doi:10.1007/s11242-014-0313-8",
  publisher =    "HAL CCSD; Springer Verlag",
  annote =       "Indian Institute of Technology; Nanyang Technological
                 University (NTU); Department of Civil, Environmental,
                 Architectural Engineering and Mathematics ;
                 Universit{\`a} degli Studi di Brescia; BotAnique et
                 BioinforMatique de l'Architecture des Plantes (AMAP) ;
                 Universit{\'e} Montpellier 2 - Sciences et Techniques
                 (UM2) - Institut national de la recherche agronomique
                 (INRA) - Institut de recherche pour le
                 d{\'e}veloppement [IRD] - Centre de coop{\'e}ration
                 internationale en recherche agronomique pour le
                 d{\'e}veloppement (CIRAD) - Centre National de la
                 Recherche Scientifique (CNRS); Singapore Ministry of
                 Education Academic Research Fund",
  bibsource =    "OAI-PMH server at api.archives-ouvertes.fr",
  contributor =  "BotAnique et BioinforMatique de l'Architecture des
                 Plantes",
  identifier =   "hal-01268778; DOI : 10.1007/s11242-014-0313-8;
                 PRODINRA : 274701",
  language =     "en",
  oai =          "oai:HAL:hal-01268778v1",
  relation =     "info:eu-repo/semantics/altIdentifier/doi/10.1007/s11242-014-0313-8",
  abstract =     "Soil water retention curves are a key constitutive law
                 used to describe the physical behaviour of an
                 unsaturated soil. Various computational modelling
                 techniques, that formulate retention curve models, are
                 mostly based on existing soil databases, which rarely
                 consider any effect of stress on the soil water
                 retention. Such effects are crucial in the case of
                 swelling soils. This study illustrates and explores the
                 ability of computational intelligence-based genetic
                 programming to formulate the mathematical relationship
                 between the water content, in terms of degree of
                 saturation, and two input variables, i.e., net stress
                 and suction for three different soils (sand--kaolin
                 mixture, Gaduk Silt and Firouzkouh clay). The
                 predictions obtained from the proposed models are in
                 good agreement with the experimental data. The
                 parametric and sensitivity analysis conducted validates
                 the robustness of our proposed model by unveiling
                 important parameters and hidden non-linear
                 relationships.",
  notes =        "also known as \cite{oai:HAL:hal-01268778v1}",
}

Genetic Programming entries for Ankit Garg Akhil Garg Kang Tai S Barontini Alexia Stokes

Citations