Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Vojtech Havlicek and Martin Hanel and Petr Maca and 
                 Michal Kuraz and Pavel Pech",
  title =        "Incorporating basic hydrological concepts into genetic
                 programming for rainfall-runoff forecasting",
  journal =      "Computing",
  year =         "2013",
  volume =       "95",
  number =       "1supplement",
  pages =        "363--380",
  month =        may,
  note =         "Special Issue on ESCO2012.",
  keywords =     "genetic algorithms, genetic programming, SORD!",
  bibdate =      "Wed Jan 29 10:23:33 MST 2014",
  bibsource =    ";
  acknowledgement = "Nelson H. F. Beebe, University of Utah, Department
                 of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake
                 City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1
                 801 581 4148, e-mail: \path||,
                 \path||, \path||
                 (Internet), URL:
  ISSN =         "0010-485X",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1007/s00607-013-0298-0",
  size =         "18 pages",
  abstract =     "This paper focuses on improving rainfall-runoff
                 forecasts by a combination of genetic programming (GP)
                 and basic hydrological modelling concepts. GP is a
                 general optimisation technique for making an automated
                 search of a computer program that solves some
                 particular problem. The SORD! program was developed for
                 the purposes of this study (in the R programming
                 language). It is an implementation of canonical GP.
                 Special functions are used for a combined approach of
                 hydrological concepts and GP. The special functions are
                 a reservoir model, a simple moving average model, and a
                 cumulative sum and delay operator. The efficiency of
                 the approach presented here is tested on runoff
                 predictions for five catchments of various sizes. The
                 input data consists of daily rainfall and runoff
                 series. The forecast step is one day. The performance
                 of the proposed approach is compared with the results
                 of the artificial neural network model (ANN) and with
                 the GP model without special functions. GP combined
                 with these concepts provides satisfactory performance,
                 and the simulations seem to be more accurate than the
                 results of ANN and GP without these functions. An
                 additional advantage of the proposed approach is that
                 it is not necessary to determine the input lag, and
                 there is better convergence. The SORD! program provides
                 an easy-to-use alternative for data-oriented modelling
                 combined with simple concepts used in hydrological
  notes =        "R programming language.

                 correct acknowledgement is: This work was supported by
                 the Technology Agency of the Czech Republic, grant
                 TA02020139. The authors wish to acknowledge the MOPEX
                 project staff, which are associated with data providing
                 and management.",

Genetic Programming entries for Vojtech Havlicek Martin Hanel Petr Maca Michal Kuraz Pavel Pech