Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming

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

@Article{Mediero:2012:NHESS,
  author =       "L. Mediero and L. Garrote and A. Chavez-Jimenez",
  title =        "Improving probabilistic flood forecasting through a
                 data assimilation scheme based on genetic programming",
  journal =      "Natural Hazards and Earth System Sciences",
  year =         "2012",
  volume =       "12",
  number =       "12",
  pages =        "3719--3732",
  month =        "19 " # dec,
  note =         "Special Issue",
  publisher =    "Copernicus GmbH",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1561-8633",
  bibsource =    "OAI-PMH server at www.doaj.org",
  language =     "eng",
  oai =          "oai:doaj-articles:20b906057483acdeeac5ddd635567115",
  URL =          "http://www.nat-hazards-earth-syst-sci.net/12/3719/2012/nhess-12-3719-2012.pdf",
  DOI =          "doi:10.5194/nhess-12-3719-2012",
  size =         "14 pages",
  abstract =     "Opportunities offered by high performance computing
                 provide a significant degree of promise in the
                 enhancement of the performance of real-time flood
                 forecasting systems. In this paper, a real-time
                 framework for probabilistic flood forecasting through
                 data assimilation is presented. The distributed
                 rainfall-runoff real-time interactive basin simulator
                 (RIBS) model is selected to simulate the hydrological
                 process in the basin. Although the RIBS model is
                 deterministic, it is run in a probabilistic way through
                 the results of calibration developed in a previous work
                 performed by the authors that identifies the
                 probability distribution functions that best
                 characterise the most relevant model parameters.
                 Adaptive techniques improve the result of flood
                 forecasts because the model can be adapted to
                 observations in real time as new information is
                 available. The new adaptive forecast model based on
                 genetic programming as a data assimilation technique is
                 compared with the previously developed flood forecast
                 model based on the calibration results. Both models are
                 probabilistic as they generate an ensemble of
                 hydrographs, taking the different uncertainties
                 inherent in any forecast process into account. The
                 Manzanares River basin was selected as a case study,
                 with the process being computationally intensive as it
                 requires simulation of many replicas of the ensemble in
                 real time.",
  notes =        "http://www.natural-hazards-and-earth-system-sciences.net/",
}

Genetic Programming entries for Luis Jesus Mediero Orduna Luis Maria Garrote De Marcos Adriadna del Socorro Chavez Jimenez

Citations