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