A Genetic Programming Approach to Rainfall-Runoff Modelling

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Savic1999219,
  author =       "Dragan A. Savic and Godfrey A. Walters and 
                 James W. Davidson",
  title =        "A Genetic Programming Approach to Rainfall-Runoff
                 Modelling",
  journal =      "Water Resources Management",
  year =         "1999",
  volume =       "13",
  number =       "3",
  pages =        "219--231",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, Computer
                 simulation, Computer systems programming, Correlation
                 methods, Hydrology, Mathematical models, Neural
                 networks, Rain, Runoff, Strategic planning, Sustainable
                 development, Watersheds, Catchments, Genetic
                 programming, Water resources, artificial neural
                 network, hydrological model, rainfall-runoff modeling,
                 sustainable development, water resource, Artificial
                 neural networks, Identification, Rainfall-runoff
                 modelling",
  publisher =    "Kluwer Academic Publishers",
  ISSN =         "0920-4741",
  URL =          "http://link.springer.com/article/10.1023%2FA%3A1008132509589",
  DOI =          "doi:10.1023/A:1008132509589",
  size =         "13 pages",
  abstract =     "Planning for sustainable development of water
                 resources relies crucially on the data available.
                 Continuous hydrologic simulation based on conceptual
                 models has proved to be the appropriate tool for
                 studying rainfall-runoff processes and for providing
                 necessary data. In recent years, artificial neural
                 networks have emerged as a novel identification
                 technique for the modelling of hydrological processes.
                 However, they represent their knowledge in terms of a
                 weight matrix that is not accessible to human
                 understanding at present. This paper introduces genetic
                 programming, which is an evolutionary computing method
                 that provides a 'transparent' and structured system
                 identification, to rainfall-runoff modelling. The
                 genetic-programming approach is applied to flow
                 prediction for the Kirkton catchment in Scotland
                 (U.K.). The results obtained are compared to those
                 attained using two optimally calibrated conceptual
                 models and an artificial neural network. Correlations
                 identified using data-driven approaches ( genetic
                 programming and neural network) are surprising in their
                 consistency considering the relative size of the models
                 and the number of variables included. These results
                 also compare favourably with the conceptual models.
                 Planning for sustainable development of water resources
                 relies crucially on the data available. Continuous
                 hydrologic simulation based on conceptual models has
                 proved to be the appropriate tool for studying
                 rainfall-runoff processes and for providing necessary
                 data. In recent years, artificial neural networks have
                 emerged as a novel identification technique for the
                 modelling of hydrological processes. However, they
                 represent their knowledge in terms of a weight matrix
                 that is not accessible to human understanding at
                 present. This paper introduces genetic programming,
                 which is an evolutionary computing method that provides
                 a `transparent' and structured system identification,
                 to rainfall-runoff modelling. The genetic-programming
                 approach is applied to flow prediction for the Kirkton
                 catchment in Scotland (U.K.). The results obtained are
                 compared to those attained using two optimally
                 calibrated conceptual models and an artificial neural
                 network. Correlations identified using data-driven
                 approaches (genetic programming and neural network) are
                 surprising in their consistency considering the
                 relative size of the models and the number of variables
                 included. These results also compare favourably with
                 the conceptual models.",
  affiliation =  "Sch. of Eng. and Computer Science, Department of
                 Engineering, University of Exeter, North Park Road,
                 Exeter EX4 4QF, United Kingdom",
  correspondence_address1 = "Savic, D.A.; School of Eng. and Computer
                 Science, Department of Engineering, University of
                 Exeter, Harrison Building, North Park Road, Exeter EX4
                 4QF, United Kingdom; email: D.Savic@exeter.ac.uk",
  language =     "English",
  document_type = "Article",
}

Genetic Programming entries for Dragan Savic Godfrey A Walters J W Davidson

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