Toward improving the reliability of hydrologic prediction: Model structure uncertainty and its quantification using ensemble-based genetic programming framework,

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

@Article{Parasuraman:2008:WRR,
  author =       "Kamban Parasuraman and Amin Elshorbagy",
  title =        "Toward improving the reliability of hydrologic
                 prediction: Model structure uncertainty and its
                 quantification using ensemble-based genetic programming
                 framework,",
  journal =      "Water Resources Research",
  year =         "2008",
  volume =       "44",
  pages =        "W12406",
  month =        "5 " # dec,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.agu.org/pubs/crossref/2008/2007WR006451.shtml",
  DOI =          "doi:10.1029/2007WR006451",
  abstract =     "Uncertainty analysis is starting to be widely
                 acknowledged as an integral part of hydrological
                 modelling. The conventional treatment of uncertainty
                 analysis in hydrologic modeling is to assume a
                 deterministic model structure, and treat its associated
                 parameters as imperfectly known, thereby neglecting the
                 uncertainty associated with the model structure. In
                 this paper, a modelling framework that can explicitly
                 account for the effect of model structure uncertainty
                 has been proposed. The modelling framework is based on
                 initially generating different realisations of the
                 original data set using a non-parametric bootstrap
                 method, and then exploiting the ability of the
                 self-organising algorithms, namely genetic programming,
                 to evolve their own model structure for each of the
                 resampled data sets. The resulting ensemble of models
                 is then used to quantify the uncertainty associated
                 with the model structure. The performance of the
                 proposed modelling framework is analysed with regards
                 to its ability in characterising the evapotranspiration
                 process at the Southwest Sand Storage facility, located
                 near Ft. McMurray, Alberta. Eddy-covariance-measured
                 actual evapotranspiration is modelled as a function of
                 net radiation, air temperature, ground temperature,
                 relative humidity, and wind speed. Investigating the
                 relation between model complexity, prediction accuracy,
                 and uncertainty, two sets of experiments were carried
                 out by varying the level of mathematical operators that
                 can be used to define the predict and-predictor
                 relationship. While the first set uses just the
                 additive operators, the second set uses both the
                 additive and the multiplicative operators to define the
                 predict-and-predictor relationship. The results suggest
                 that increasing the model complexity may lead to better
                 prediction accuracy but at an expense of increasing
                 uncertainty. Compared to the model parameter
                 uncertainty, the relative contribution of model
                 structure uncertainty to the predictive uncertainty of
                 a model is shown to be more important. Furthermore, the
                 study advocates that the search to find the optimal
                 model could be replaced by the quest to unearth
                 possible models for characterising hydrological
                 processes.",
}

Genetic Programming entries for Kamban Parasuraman Amin Elshorbagy

Citations