Evolutionary computation-based approach for model error correction and calibration

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Emily M. Zechman and S. Ranji Ranjithan",
  title =        "Evolutionary computation-based approach for model
                 error correction and calibration",
  journal =      "Advances in Water Resources",
  year =         "2007",
  volume =       "30",
  number =       "5",
  pages =        "1360--1370",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computation, Calibration, Model error correction",
  DOI =          "doi:10.1016/j.advwatres.2006.11.013",
  abstract =     "Calibration is typically used for improving the
                 predictability of mechanistic simulation models by
                 adjusting a set of model parameters and fitting model
                 predictions to observations. Calibration does not,
                 however, account for or correct potential
                 misspecifications in the model structure, limiting the
                 accuracy of modelled predictions. This paper presents a
                 new approach that addresses both parameter error and
                 model structural error to improve the predictive
                 capabilities of a model. The new approach
                 simultaneously conducts a numeric search for model
                 parameter estimation and a symbolic (regression) search
                 to determine a function to correct misspecifications in
                 model equations. It is based on an evolutionary
                 computation approach that integrates genetic algorithm
                 and genetic programming operators. While this new
                 approach is designed generically and can be applied to
                 a broad array of mechanistic models, it is demonstrated
                 for an illustrative case study involving water quality
                 modelling and prediction. Results based on extensive
                 testing and evaluation, show that the new procedure
                 performs consistently well in fitting a set of training
                 data as well as predicting a set of validation data,
                 and outperforms a calibration procedure and an
                 empirical model fitting procedure.",

Genetic Programming entries for Emily M Zechman S Ranji Ranjithan