Stream Flowrate Prediction Using Genetic Programming Model in a Semi-Arid Coastal Watershed

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "A. Drunpob and N. B. Chang and M. Beaman",
  title =        "Stream Flowrate Prediction Using Genetic Programming
                 Model in a Semi-Arid Coastal Watershed",
  booktitle =    "World Water and Environmental Resources Congress
  year =         "2005",
  editor =       "Raymond Walton",
  address =      "Anchorage, Alaska, USA",
  month =        may # " 15-19",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1061/40792(173)352",
  abstract =     "Effective water resources management is a critically
                 important priority across the globe. The availability
                 of adequate fresh water is a fundamental requirement
                 for the sustainability of human and terrestrial
                 landscapes, and the importance of understanding and
                 improving predictive capacity regarding all aspects of
                 the global and regional water cycle is certain to
                 continue to increase. One fundamental component of the
                 water cycle is stream discharge. Stream flowrate
                 prediction is not only related to regular water supply
                 for human, animal, and plant populations, but also
                 relevant for the management of natural hazards, such as
                 drought and flood, that occur abruptly resulting in
                 economic loss. Efforts to improve existing methods and
                 develop new methods of stream flow prediction would
                 support the optimal management of water resources at
                 all scales in space and time. Recent advances in
                 genetic programming technologies have shown potential
                 to improve the prediction accuracy of stream flow rate
                 in some river systems by better capturing the
                 non-linearity of the features embedded in a system.
                 This study elicits microclimatological factors in
                 association with the basin-wide geological environment,
                 exhibits the derivation of a representative genetic
                 programming model, summarises the non-linear behaviour
                 between the rainfall/run-off patterns, and conducts
                 stream flow rate prediction in a river system given the
                 influence of dynamic basin features such as soil
                 moisture, soil texture, vegetative cover, air
                 temperature, and precipitation rate. Three weather
                 stations are deployed as a supplementary data-gathering
                 network in addition to over 10 existing gage stations
                 in the semi-arid Nueces River Basin, South Texas. An
                 integrated database of physical basin features is
                 developed and used to support a semi-structure genetic
                 programming modelling approach to perform stream
                 flowrate predictions. The genetic programming model is
                 eventually proved useful in forecasting stream flowrate
                 in the study area where water resources scarce issues
                 are deemed critical.",
  notes =        "c2005 ASCE",

Genetic Programming entries for Ammarin Drunpob Ni-Bin Chang Mark Beaman