Multi-objective management of saltwater intrusion in coastal aquifers using genetic programming and modular neural network based surrogate models

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

@Article{Sreekanth2010245,
  author =       "J. Sreekanth and Bithin Datta",
  title =        "Multi-objective management of saltwater intrusion in
                 coastal aquifers using genetic programming and modular
                 neural network based surrogate models",
  journal =      "Journal of Hydrology",
  volume =       "393",
  number =       "3-4",
  pages =        "245--256",
  year =         "2010",
  ISSN =         "0022-1694",
  DOI =          "doi:10.1016/j.jhydrol.2010.08.023",
  URL =          "http://www.sciencedirect.com/science/article/B6V6C-50X3N5F-6/2/07d7a64a570c1efe71d9b3a1c71ffb34",
  keywords =     "genetic algorithms, genetic programming, Salinity
                 intrusion, Coastal aquifer, Pumping optimisation,
                 Surrogate model, Modular neural network",
  abstract =     "Surrogate model based methodologies are developed for
                 evolving multi-objective management strategies for
                 saltwater intrusion in coastal aquifers. Two different
                 surrogate models based on genetic programming (GP) and
                 modular neural network (MNN) are developed and linked
                 to a multi-objective genetic algorithm (MOGA) to derive
                 the optimal pumping strategies for coastal aquifer
                 management, considering two objectives. Trained and
                 tested surrogate models are used to predict the
                 salinity concentrations at different locations
                 resulting due to groundwater extraction. A two-stage
                 training strategy is implemented for training the
                 surrogate models. Surrogate models are initially
                 trained with input patterns selected uniformly from the
                 entire search space and optimal management strategies
                 based on the model predictions are derived from the
                 management model. A search space adaptation and model
                 retraining is performed by identifying a modified
                 search space near the initial optimal solutions based
                 on the relative importance of the variables in salinity
                 prediction. Retraining of the surrogate models is
                 performed using input-output samples generated in the
                 modified search space. Performance of the methodologies
                 using GP and MNN based surrogate models are compared
                 for an illustrative study area. The capability of GP to
                 identify the impact of input variables and the
                 resulting parsimony of the input variables helps in
                 developing efficient surrogate models. The developed GP
                 models have lesser uncertainty compared to MNN models
                 as the number of parameters used in GP is much lesser
                 than that in MNN models. Also GP based model was found
                 to be better suited for optimisation using adaptive
                 search space.",
}

Genetic Programming entries for Janardhanan Sreekanth Bithin Datta

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