A data-driven approach for modeling post-fire debris-flow volumes and their uncertainty

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@Article{Friedel20111583,
  author =       "Michael J. Friedel",
  title =        "A data-driven approach for modeling post-fire
                 debris-flow volumes and their uncertainty",
  journal =      "Environmental Modelling \& Software",
  volume =       "26",
  number =       "12",
  pages =        "1583--1598",
  year =         "2011",
  ISSN =         "1364-8152",
  DOI =          "doi:10.1016/j.envsoft.2011.07.014",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1364815211001757",
  keywords =     "genetic algorithms, genetic programming, Wildfire,
                 Debris-flow volume, Self-organising map, Multivariate,
                 Prediction, Nonlinear models, Nonlinear uncertainty",
  abstract =     "This study demonstrates the novel application of
                 genetic programming to evolve nonlinear post-fire
                 debris-flow volume equations from variables associated
                 with a data-driven conceptual model of the western
                 United States. The search space is constrained using a
                 multi-component objective function that simultaneously
                 minimises root-mean squared and unit errors for the
                 evolution of fittest equations. An optimisation
                 technique is then used to estimate the limits of
                 nonlinear prediction uncertainty associated with the
                 debris-flow equations. In contrast to a published
                 multiple linear regression three-variable equation,
                 linking basin area with slopes greater or equal to 30
                 percent, burn severity characterised as area burned
                 moderate plus high, and total storm rainfall, the
                 data-driven approach discovers many nonlinear and
                 several dimensionally consistent equations that are
                 unbiased and have less prediction uncertainty. Of the
                 nonlinear equations, the best performance (lowest
                 prediction uncertainty) is achieved when using three
                 variables: average basin slope, total burned area, and
                 total storm rainfall. Further reduction in uncertainty
                 is possible for the nonlinear equations when
                 dimensional consistency is not a priority and by
                 subsequently applying a gradient solver to the fittest
                 solutions. The data-driven modelling approach can be
                 applied to nonlinear multivariate problems in all
                 fields of study.",
}

Genetic Programming entries for Michael J Friedel

Citations