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

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@InProceedings{Drunpob:2005:WWERC,

• 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 2005",
• 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

Citations